Automated detection of high-frequency oscillations in

Journal of Physiology - Paris 110 (2016) 316–326

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Original Research Paper

Automated detection of high-frequency oscillations in electrophysiological signals: Methodological advances Miguel Navarrete b, Jan Pyrzowski a, Juliana Corlier a, Mario Valderrama b,⇑, Michel Le Van Quyen a,⇑ a b

Institut du Cerveau et de la Moelle Epinière, UMR S 1127, CNRS UMR 7225, Hôpital de la Pitié-Salpêtrière, Paris, France Department of Biomedical Engineering, University of Los Andes, Bogotá D.C., Colombia

a r t i c l e

i n f o

Article history: Received 30 May 2016 Received in revised form 31 January 2017 Accepted 19 February 2017 Available online 21 February 2017 Keywords: High frequency oscillations HFO Gamma Ripple Fast ripple Epilepsy Microelectrodes Macroelectrodes iEEG EEG

a b s t r a c t In recent years, new recording technologies have advanced such that oscillations of neuronal networks can be identified from simultaneous, multisite recordings at high temporal and spatial resolutions. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods capable of extracting meaningful information related to time, frequency and space. In this review, we aim to bridge this gap by focusing on the new analysis tools developed for the automated detection of highfrequency oscillations (HFOs, >40 Hz) in local field potentials. For this, we provide a revision of different aspects associated with physiological and pathological HFOs as well as the several stages involved in their automatic detection including preprocessing, selection, rejection and analysis through time-frequency processes. Beyond basic research, the automatic detection of HFOs would greatly assist diagnosis of epilepsy disorders based on the recognition of these typical pathological patterns in the electroencephalogram (EEG). Also, we emphasize how these HFO detection methods can be applied and the properties that might be inferred from neuronal signals, indicating potential future directions. Ó 2017 Published by Elsevier Ltd.

1. Introduction Advances in our understanding of neural systems go hand-inhand with improvements in experimental techniques used to study them. Over the past three decades, the rise of broad-band digital systems has enabled the monitoring of electrophysiological signals beyond the traditional low-pass filtered EEG at 40 Hz, extending the recordings to frequencies as high as 500 Hz or more (Menendez de la Prida et al., 2015; Worrell et al., 2012). Based on these modern recording techniques, high-frequency oscillations (HFOs) ranging from 40 to 800 Hz have been observed in extracellular field potentials of small clusters of synchronized neurons (Buzsáki and Lopes da Silva, 2012; Jefferys et al., 2012). Hence, given the high resistivity of the skull and the assumption that a large extent of cortex is needed to observe an event on the scalp, HFOs have been mostly identified in local field potentials recorded by invasive microelectrodes, which placed directly in contact with brain tissues are able to intercept signals from small groups of neurons (Collura et al., 1990; Engel and Lopes da Silva, 2012; Wong, 1996). In this context, basic research studies have indicated that ⇑ Corresponding authors. E-mail address: [email protected] (M. Le Van Quyen). http://dx.doi.org/10.1016/j.jphysparis.2017.02.003 0928-4257/Ó 2017 Published by Elsevier Ltd.

HFOs underlie several physiological functions of the normal brain (Buzsaki and Draguhn, 2004; Canolty et al., 2006; Carr et al., 2012; Colgin et al., 2009; Colgin and Moser, 2010; Fries, 2009; Fries et al., 2001; Tallon-Baudry and Bertrand, 1999), but they are also a characteristic feature of several neurological diseases (Uhlhaas and Singer, 2012). In particular, early studies in epileptic subcortical limbic and neocortical structures revealed that HFOs > 250 Hz reflect basic neuronal disturbances responsible for epilepsy and provide a precise biomarker of the epileptogenic zone (Le Van Quyen, 2012). With the continuous improvement in the design of the clinical EEG systems that routinely support wide bandwidth recordings, current patient studies show that intracranial HFOs provide a tremendous amount of new information regarding epilepsy that is enormous useful to predict, diagnose and treat this disease more efficiently (Dümpelmann et al., 2015; Fedele et al., 2016; Höller et al., 2015; Jacobs et al., 2012; Zijlmans et al., 2012). Nevertheless, in spite of the recent advances in hardware and electrodes designed specifically to capture HFOs, these events are challenging to detect because of the relative low signal to noise compared to other interictal epileptiform discharges (Amiri et al., 2016; Xiang et al., 2014; Zijlmans et al., 2011), they can occur as brief bursts lasting 30 ms or less, and they are found in brain areas capable of generating seizures

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(Alvarado-Rojas et al., 2015; Le Van Quyen et al., 2006). Overcoming these issues will require firstly the improvement of algorithms capable of reliably detecting HFOs in continuous wide bandwidth EEG recorded from microelectrodes and conventional clinical electrodes, and secondly the development of analysis tools that can accurately characterize and quantify HFOs (Menendez de la Prida et al., 2015; Navarrete et al., in press). Consequently, in the last years it has been a growing interest in the development of new analytical mechanisms to improve the automatic detection of HFOs. Nevertheless, even though these methodological tools share common features, their mathematical particularities might conceal some important characteristics that may be crucial when applying them in a specific context of HFO analysis. For this reason, in this review we would like to draw attention to the techniques that different research groups have developed to automatically detect this type of events. In the coming section, we introduce normal and pathological HFOs in further detail. Subsequently, we describe the morphology of the HFO signal in order to latterly review the common and particular characteristics of most of the automatic methods for the detection of HFOs published so far.

2. Normal and pathologic HFOs in the mammalian brain HFOs includes all physiological and pathological oscillatory activities which have a power increase in a limited frequency band inside the 40–800 Hz band and persisting during tens of millisec-

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onds (Aivar et al., 2014; Ren et al., 2015). As displayed in Fig. 1, in vitro and in vivo studies revealed the presence of several different types of HFOs: depending on their frequency range, these events are classified as gamma oscillations covering low gamma (40–80 Hz) and high gamma (80–120 Hz), ripples (120–240 Hz) and fast ripple (>240 Hz). In the recent years, gamma oscillations have been subject to increasing interest. Several studies have distinguished broadband high-gamma activity from narrowband gamma rhythms (Manning et al., 2009; Miller et al., 2007). Broadband gamma activity extends over all the gamma frequency range without a defined frequency and phase; conversely, a narrowband gamma rhythm is well determined by an oscillatory activity (Hermes et al., 2015; Lachaux et al., 2012). For this reason, these two gamma activities have been suggested to be caused from different biophysical origins (Jerbi et al., 2009; Ray and Maunsell, 2011). In general, gamma activity is hypothesized to be important in a range of waking functions; for instance, it has been reported an increase in the broadband gamma activity during sensory binding (Lachaux et al., 2005; Singer and Gray, 1995) and cognitive processing (Bastin et al., 2016; Canolty et al., 2006); similarly, the enhancement of narrowband gamma rhythm has been observed during attention (Fries et al., 2001), encoding and retrieval of memory traces (Montgomery and Buzsáki, 2007) and large-scale integration in the cortex (Corlier et al., 2016). Despite numerous studies regarding the role of gamma on information processing during wakefulness, these rhythms also occur during deep anesthesia as well as natural sleep (Destexhe et al., 1999). Recently, further works using

Fig. 1. Classification of HFOs. HFOs are categorized according to their dominant frequency such as gamma oscillations (40–120 Hz), ripples (120–240 Hz) and fast ripples (>240 Hz). These oscillations can be physiological or pathological: In-vivo physiological gamma oscillations were described in Chrobak and Buzsáki (1998) and ripples in Buzsáki et al. (1992). On the other hand, pathological fast ripples were described in Bragin et al. (1999a, 1999b) and epileptic gamma oscillations in Worrell et al. (2004). The HFO frequency relates to the spatial scale of the neural oscillators that produce this activity. Therefore, their respective identification would depend on the size of recording electrode. For instance, it is difficult to find oscillatory activity >120 Hz in scalp EEG or activity >500 Hz in iEEG.

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microelectrodes and intracranial EEG (iEEG) recordings in the cortex of epileptic patients have verified the strong presence of gamma oscillations during slow wave sleep (Le Van Quyen et al., 2010; Valderrama et al., 2012). For all these manifestations, the gamma activities are thought to provide a temporal structure relative to the activities of individual neurons, which are organized in a millisecond timescale across distributed neural networks (Chrobak and Buzsáki, 1998; Nikolic´ et al., 2013). Ripples were originally described as physiological activity in hippocampus. Hippocampal ripples are usually associated with sharp-waves (Buzsáki et al., 1992) that occur in rodent hippocampal-entorhinal networks during immobility or slowwave sleep (O’Keefe and Nadel, 1978). Sharp-wave ripple complexes originate from the synchronized firing of CA3 cells and spread downstream as spatially coherent oscillations along the CA1-subicular-entorhinal axis (Chrobak and Buzsáki, 1996). This co-activation of hippocampal and neocortical pathways is thought to be crucial for memory consolidation processes during which memories are gradually translated from short-term hippocampal to longer-term neocortical stores (Lee and Wilson, 2002; Wilson and McNaughton, 1994). Closely related to ripples, the fast ripples (FR) were first recorded during interictal periods using microelectrodes from the hippocampus and entorhinal cortex of patients with mesial temporal lobe (MTL) epilepsy (Bragin et al., 1999a, 1999b), and similar oscillations were detected in rodent models of MTL epilepsy (Bragin et al., 2002; Foffani et al., 2007). Like ripples, FRs are more strongly expressed during quiet wakefulness and slow-wave sleep. Nonetheless, besides the frequency range, FRs differ from ripples by the fact that they can be recorded from dentate gyrus where normal ripples never occur, and they seem to be localized exclusively in areas capable of generating spontaneous seizures. For instance, FRs are recorded exclusively from epileptogenic areas of the lesioned hippocampus, and they occur in the epileptogenic MTL containing atrophic hippocampi and amygdala at higher frequencies than in the contralateral MTL (Staba et al., 2004). Consequently, they provide a promising diagnostic marker for epileptogenic areas that are associated with hippocampal sclerosis and consequent synaptic reorganization. For these reasons, FRs are thought to be generated by small discrete clusters of neurons, whereas generation of ripples is much more diffuse (Bragin et al., 2002). Because HFOs were shown to strongly co-localize with the seizure onset zone (Crépon et al., 2010; Jacobs et al., 2008, 2009) and to have more stable distributions over time than interictal epileptic discharges (IEDs) (Zijlmans et al., 2011), they are particularly important for epilepsy surgery – a procedure which aims at entirely removing the ‘‘minimum amount of cortex required to produce seizure freedom” also called the ‘‘epileptogenic zone”. This pre-surgical workup is a lengthy procedure and a heavy burden for the patients, often requiring more than a week of continuous invasive iEEG to register a sufficient number of stereotyped seizures. The interest in HFOs as markers of epileptogenic areas in presurgical patients has driven the development of many clinical electrophysiology systems used with standard clinical intracranial electrodes to support higher sampling rates (>1000 Hz) and provide greater bandwidth. Importantly, different studies using these systems confirm that HFOs can be recordable with standard clinical macro-electrodes (Jirsch et al., 2006), partially overturning a strong belief that they can be produced only by extremely small sub-millimeter generator networks (Bragin et al., 2002). Above all, however, resections involving a large proportion HFOdisplaying electrode positions (the ‘‘HFO-zone”) were found to be associated with a favorable surgical outcome (Akiyama et al., 2011; Cho et al., 2014; Fujiwara et al., 2012; Jacobs et al., 2010; Kerber et al., 2014) and the statistical significance of this associa-

tion has been confirmed by a recent meta-analysis (Höller et al., 2015). More recently it became conceivable that interictal HFOs may also be detectable at the scalp level (Andrade-Valenca et al., 2011; Melani et al., 2013; Pizzo et al., 2016; Zelmann et al., 2014). For instance, a scalp study on patients with absence epilepsy found HFOs associated with generalized spike discharges (Chaitanya et al., 2015). Other simultaneous scalp and intracranial study (Zelmann et al., 2014) has shown that HFOs are visible in scalp tracings even though their intracranial counterparts likely originate from small generator networks. Additionally, the much lower rates of scalp-recorded HFOs have been suggested to reflect spatial under-sampling, and if this were to be true, it could be overcome by the use of dense-array EEG (Lantz et al., 2003). However, these findings remain controversial as they challenge the traditional view on the relationship between scalp and intracranial signals according to which only relatively large-scale discharges should be visible in the scalp EEG (Cooper et al., 1965; Tao et al., 2005). When attempting to identify scalp HFOs, special care must be taken to exclude artefactual events, especially those of muscular origin (Andrade-Valenca et al., 2011). HFOs are also detectable in magnetoencephalography (MEG) (Miao et al., 2014; Papadelis et al., 2009; Rampp et al., 2010; Tenney et al., 2014; von Ellenrieder et al., 2016b; Xiang et al., 2009). The study of HFOs on MEG recordings mainly relies on time-frequency analyses, yet it is not known whether wellcharacterized time-domain HFOs can be detected from noninvasive MEG recordings, limiting the scope of these studies on research and clinics (van Klink et al., 2016; Xiang et al., 2014). Accordingly, the analysis of this type of recordings requires the development of new methodological techniques; for instance, the standard equivalent current dipole model used for source estimation might not be suitable for the localization of low amplitude activity such as HFOs (Sakuma et al., 1999). In this sense, the implementation of beamformer techniques which create virtual sensors in order to improve the MEG time domain signals seems to be promising to this aspect (Nissen et al., 2016).

3. What is a HFO? As a general rule, HFOs are physiological events with limited duration and frequency components (Worrell et al., 2012). It is important to consider that the time involving a HFO depends on the minimal frequency component that outlines the event, so an event in the gamma band may last some hundreds of milliseconds while a fast ripple event may last a few milliseconds. Nevertheless, it is often difficult to determine a clear frequency cut-off between the different types of oscillations. Indeed, HFOs reflect a continuum of cellular processes contributing to overlapping frequencies from the lower 40 Hz band to the very fast frequency bands. Given their complex neuronal nature, HFOs are extremely sensitive to recording conditions. This fact arises because HFOs are the consequence of the activity of a population of neurons with different levels of synchronization, where their electrical trace varies according to different factors such as the electrode size (macro-electrodes, micro-electrodes), the recording type (in vivo, in vitro), the neural tissue where the recording is done (neocortical, hippocampal) and the distance between the electrode and the HFO generators. Consequently, HFOs are not sinusoid-like field potentials but they may be shaped by multiples frequency components varying across the time (Menendez de la Prida et al., 2015). Another difficulty for HFOs definition is related to their amplitudes. Some studies state that HFOs may be so small that its oscillatory activity might not be seen in the raw EEG but is well defined

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in a determined frequency band (Burnos et al., 2016; Jacobs et al., 2012), others do not take into account the raw data (Haufler et al., 2014; Wang et al., 2014) while other studies rejects all events where a presence of the oscillatory activity is not a clear on the raw signal (Birot et al., 2013; Crépon et al., 2010; Valderrama et al., 2012). Generally, after preprocessing, the HFO amplitude is considered to vary between 10 and 1000 lV (Worrell et al., 2012). However, the analysis of HFO amplitudes must be taken carefully because they might be corrupted by broad frequency components of spiking activity, multi-unit activity in the high frequency range and artifacts (Menendez de la Prida et al., 2015). 4. Automatic techniques for HFO detection The current exponential growth in multichannel measurement precludes exhaustive manual inspection by the human experimenter, and it has significantly increased the need for automatic analysis techniques. For this reason, several algorithms have been developed by different research centers during the last years. Table 1 presents a summary of most of the automatic detectors published so far, mostly in the field of epilepsy research. Nineteen studies which have been published since 2002 to automatically detect HFOs in invasive microelectrodes LFP or intracranial EEG are included in this table. In all these attempts, the methods are prone to false detections and are susceptible to detection of spurious HFOs that can arise from a variety of sources (see Fig. 2). First, the appearance of oscillations in bandpass-filtered data can be induced by aliasing artefacts of filtering or harmonics of other frequency components. For example, spurious oscillations can arise due to rapid transitions in the signal, asymmetry of rhythmic oscillations, large amplitude EEG spikes and multiunit activity picked up in the recordings. Second, the presence of noise, mobile trends and multiple oscillations might destroy the discernibility of spectral peaks representing rhythmic activity. Finally, calibration is often performed in a semi-supervised way, whereby human experts review sampled candidate events identified by the automated detector. Likewise, the methods are optimized for a particular database and the detection is limited by the investigator experience, inducing possible bias, low inter-rater reliability and problems with reproducibility (Menendez de la Prida et al., 2015). Despite these potential pitfalls, a consensus has emerged across the different methods to apply three main steps: (i) a preprocessing process; (ii) the selection of the events from background; and (iii) the rejection of false oscillations. Different comparisons of automatic HFOs detection methods have been developed evaluating the strengths and weaknesses of some of the algorithms here presented (Balach et al., 2015; Chaibi et al., 2013; Salami et al., 2012; Zelmann et al., 2012). Chiefly, these comparison studies present similar performances across databases with modest differences between the analyzed methods. 4.1. The preprocessing process Preprocessing is the first step of any method for HFO detection. Most of the time, data are bandpass-filtered to restrict the range of frequencies under consideration, to detect events represented by a particular ‘characteristic’ frequency and to remove any unwanted signals. Additional filtering (e.g. spectral equalization to compensate for the spectral roll-off present in field activity) can also be performed to remove interfering activities. Nevertheless, in most of the cases, the filtering process may contaminate the data with spurious oscillations because of the frequency components of artifacts (Bénar et al., 2010). Furthermore, if the applied filter is not appropriate, real oscillatory components could be distorted affect-

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ing the performance of the analysis method (Widmann et al., 2014). In general, it is important to note that any filter is going to modify the oscillatory components of the frequency band of interest. Therefore, it is necessary to choose a filter configuration that has the capability to minimize the signal distortions. In order to obtain reliable components, here is a list of recommendations: First, the maximum analyzed frequency must be less than the Nyquist frequency (that is to say: 1/2 de sampling frequency). Nevertheless, oscillations near the Nyquist frequency should be analyzed carefully because they might have not enough information of the original signal. From the Nyquist sampling, there is not a rule of thumb to know a priori which maximal frequency is possible to detect. Second, a well-designed filter requires a flat top frequency response. Filters with ripple-like frequency responses on the band-pass band could strongly affect the amplitude of the oscillations in the band of interest. Third, the cut-off frequencies and the filter order should be selected such as the frequencies located at the borders of the band of interest are only minimally affected by the roll-off of the transition bandwidth. Here, it is important to stress that cutoff frequencies are attenuated by at less half of the amplitude compared to those frequencies in the middle of the frequency band. Fourth, linear-phase or minimum-phase filters should be preferred. Five, it is important to check that the designed filter is stable; causal filters (e.g. Windowed filters, ParksMcClellan, etc.) are always stable but non-causal filters (e.g. Butterworth, Chebyshev, etc.) might be unstable. If non-causal filters are used, it is recommended to use a second orders sections configuration to guarantee stability. Last, filters should be applied to continuous data rather than segmented or discontinued data; this will avoid filter artifacts due to signal discontinuities and DC offset corrections (Widmann et al., 2014). Additionally, when large data sets are analyzed, adapted strategies have been proposed to address many of the difficulties encountered with the long-term fluctuating rate of HFO occurrences. For instance, a solution to this issue is to analyze the data by epochs (Gardner et al., 2007). This way allows the algorithm to adapt some detection parameters according to the local characteristics of the analyzed data. Even so, as energy varies for different epochs, non-adjustable detection parameters which are optimal for some epochs might not be optimal for others. Adaptive methods for thresholding may reduce eventual unpredictability of energy variance along epochs and increase the sensitivity in the detection (von Ellenrieder et al., 2012). Nonetheless, it is not clear yet how the energy variability across epochs influences the outcome of the HFO analysis in long term recordings. According to Table 1, some common values for epochs in the HFO detection of long term recordings are 10 s, 1 min and 3 min. 4.2. Selection of the events from background The selection of events from background is a two-step process: (i) the implementation of a method to monitor the energy variations of oscillatory activity in the frequency band of interest, and (ii) the implementation of a procedure to highlight the events within this same frequency band. For the first step (i), three distinct methods have been proposed to monitor the energy variations of the oscillatory activity in the frequency band of interest (see Fig. 3): the root mean squared (RMS) energy (Birot et al., 2013; Blanco et al., 2010; Dümpelmann et al., 2012; Gliske et al., in press; Staba et al., 2002; von Ellenrieder et al., 2012), the line-length energy (Dümpelmann et al., 2012; Gardner et al., 2007; Matsumoto et al., 2013) and the Hilbert transform envelope (Burnos et al., 2014, 2016; Crépon et al., 2010). The RMS is a statistical measure of the variation of set of numbers of a variable, and it is defined

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Table 1 Summary of most of the automatic HFOs detectors published since 2002. Included here are seventeen studies with their main characteristics. FR: Fast Ripple, R: Ripple. Authors

Preprocessing

Detections of events from background

Rejection of false events

Results Sensitivity

Specificity

Event time and number of oscillations

84%

Non-reported

Non-reported Event time

Nonreported 89.50%

Non-reported

Yes

Local maxima in the normalized timefrequency transform (Morlet Wavelet) Frequency equalization/line-length energy

180–400 Hz

Yes

Hilbert envelope

Event time

100%

90.50%

All signal

100–500 Hz

Yes

RMS amplitudes

80–450 Hz

Yes

Wavelet entropy/RMS amplitudes

Nonreported 91%

Non-reported

10 s/all signal All signal with adaptive thresholding

Power spectral density + k-medoids/gap-statistic clustering Event time

40–200 Hz

Yes (multiband)

RMS amplitudes with adaptive window and thresholding according to the frequency subband

90.7% ± 15.7%

Non-reported

3-min

All signal

80–250 Hz

Yes

49.10%

36.30%


1 h

All signal

250–600 Hz

Yes

None

93%

95%

Nonreported Nonreported

10 s

30–700 Hz

Yes

RMS amplitudes/line-length energy/ instantaneous frequency RMS amplitudes/Fourier or wavelet energy ratio (low frequency/high frequency) Line-length energy

Threshold of the ratio between maximum absolute values of the broadband signal and the signal in the narrow frequency sub-band/threshold of the maximum of the RMS value of the narrowband signal/threshold values optimized by expert annotations RBF neural network

Support vector machine (Linear Kernel)

83.80%

84.60%

Nonreported

80–500 Hz

No

Non-reported

88.93%

Non-reported

López-Cuevas et al. (2013) Burnos et al. (2014) Chaibi et al. (2014)

On-line

>200 Hz

No

Artificial recurrent neural network

80–500 Hz

Yes

Hilbert envelope

91%

Nonreported

Nonreported

80–500 Hz

No

96.77%

85%

Wang et al. (2014) Gliske et al. (in press)

Nonreported On-line

1-min

160–470 Hz

Yes

100–500 Hz

Yes

Nonreported 78.50%

Non-reported

10-min

Tunable Q-factor wavelet transform in conjunction with Morphological Component Analysis (MCA) and local maxima in the normalized time-frequency transform (Morlet Wavelet) Power estimation by multitaper method/ power peak detection RMS amplitudes in referential montage

Time-frequency by the Stockwell transform and morphologic characterization of events in frequency Event time

Nonreported 50%

Non-reported

5-min

Nonreported 10 s/1 min

Matching pursuit on the time-frequency transform using a dictionary of Gabor atoms (sine modulated Gaussian functions) Approximate entropy

Burnos et al. (2016)

1-min (test dataset) 1-min

All signal

80–250 Hz 250–500 Hz

Yes

Hilbert envelope

93%

Non-reported

All signal

30 Hz

58 ± 15min 10-min

All signal

Dümpelmann et al. (2012) Birot et al. (2013) Matsumoto et al. (2013) Chaibi et al. (2013)

Amiri et al. (2016) Fedele et al. (2016)

1-min 30-min

Spatial mapping Events detected by the rejection algorithm are discarded as putative events: (RMS amplitudes in average reference montage/line-length energy in the 80–100 Hz band) Event time, number of oscillations and morphologic characterization of events in time and frequency

Non-reported

91%

88.50%

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Time of segments analyzed

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Fig. 2. Examples of real and spurious HFOs identified by automatic detection methods. Along with typical HFOs in gamma, ripple and fast ripple frequency range, automated detection techniques can also give rise to false identification such as high frequency noise, artifacts and epileptic spikes. From Top to Bottom: 500 ms time segments for raw intracranial EEG recordings including detected events (in red). Filtered segments in the 80–500 Hz band. Time-frequency representations in the 80–500 Hz range for selected events. Welsh power spectrum densities (PSD) for each segment indicating the maximal frequency in the analyzed band. Note spurious HFOs arise from high pass filtering of large amplitudes activities.

by Eq. (1), where N makes reference to the total length of the signal. This measure has been used to identify other type of low frequency electrophysiological ripples such as spindles (Clemens et al., 2007; Schimicek et al., 1994). The RMS is typically computed using a sliding window of 3 ms as applied in Staba et al. (2002), but different values may be implemented depending on the frequency of interest (von Ellenrieder et al., 2012). The line-length energy is a method based on fractal dimension to estimate the amplitude variability of the signal (Esteller et al., 2001) defined by Eq. (2). This measure was previously used for seizure onset detection (Litt et al., 2001). The third method is the signal envelope computed from the Hilbert transform (Crépon et al., 2010). This transform estimates an analytical signal from the original signal which gives a complex number for each sample, from where the magnitude is computed. Nevertheless, how the implementation of any of these methods affects the efficacy of HFOs is not clear yet.

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 Xt EðtÞ ¼ x2 ðkÞ k¼tNþ1 N EðtÞ ¼

t X

jxðkÞ  xðk  1Þj

ð1Þ

ð2Þ

k¼tNþ2

Alternative methods have also been introduced to select events from background, and they can be used along with or without the three previously described methods. For instance, in conjunction

with the previous methods, some authors focus on the identification of the background signal to compare the energy ratio between putative events and the baseline activity to increase sensitivity (Fedele et al., 2016; Zelmann et al., 2012). This baseline detection may be performed by the computation of the Wavelet entropy of the signal (Rosso et al., 2001; Zelmann et al., 2010), by the energy estimation by the Fourier transform or by using wavelet filter banks (Birot et al., 2013). Stand-alone methods to monitor the energy variations of the oscillatory activity include the detection of temporal variations of the time-frequency transform of the signal (Chaibi et al., 2014; Khalilov et al., 2005), the estimation of the power spectra by multitaper estimation (Wang et al., 2014) and the energy estimation by the approximate entropy of the signal (López-Cuevas et al., 2013). For the second step (ii), a number of techniques have been proposed. Most of the detectors employ a statistical threshold to identify significant amplitude variations from the background (Fig. 3). For instance, Staba et al. (2002) and Crépon et al. (2010) proposed to retain only those events that surpass a determined order of standard deviations. Nevertheless, as Gardner et al. (2007) pointed out, the distribution of the energy variation is not normal, and the standard deviation might not be the most adequate statistic for thresholding. In order to be less susceptible to non-normal energy fluctuations, the n-th percentile of the cumulative distribution is sometimes used. The value n varies among authors, and this is a value that have been computed empirically depending on the data-

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Fig. 3. Automatic detection of HFOs by energy thresholding. (A) Example of 3 s segment of iEEG recording of a patient with epilepsy. The raw data are displayed with the corresponding filtered signal and time-frequency representation in the 80–800 Hz band. Note the complexity of frequency components associated with the 3 detected HFOs. (B–D) Expanded traces of detected HFOs showing the raw signals and several energy measures (RMS in red, Line-Length in green and Hilbert envelope in blue). In (C and D), the dashed orange lines represent the threshold in 5 standard-deviation and the dashed-dot magenta line represents the threshold in the 95-percentile. ‘‘D” represents a HFO detection and ‘‘N” a non-detection for each energy measures. Note for C that the HFO is only detected by using the 95-percentile thresholding on the RMS and Line-Length measures because the very low energy of the event.

base (Burnos et al., 2016; Gardner et al., 2007; Zelmann et al., 2012). Conversely, non-statistical methodologies include methods such as matching pursuit (Chaibi et al., 2013), morphological component analysis (Chaibi et al., 2014), moving thresholding proportional to the background signal level (von Ellenrieder et al., 2012) and the use of differential sharp event detectors (Amiri et al., 2016).

4.3. Rejection of false events After detecting putative HFOs, spurious events should be selected from real HFOs. As already pointed out, these false detections may be caused by high frequency components of artifacts, pathologic spikes or specific electrical noise (see Fig. 2). Automatic techniques to identify these false detections include methods as

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simple as checking the time over-threshold of each detected event and counting the number of oscillations (Chaibi et al., 2014; Staba et al., 2002; Zelmann et al., 2012) and by computing the ratio between maximum absolute values of the broadband signal and the filtered signal (von Ellenrieder et al., 2012), or the implementation of more complex algorithms such as clustering (Blanco et al., 2010), support vector machines (Amiri et al., 2016) which can also be useful for the differentiation between physiological and pathological HFOs (Matsumoto et al., 2013), neural networks (Dümpelmann et al., 2012; López-Cuevas et al., 2013) and morphology characterization (Burnos et al., 2016). Some authors use the global activity of multiple electrodes to differentiate activity caused by artifacts from the local activity which is typical of HFOs (Fedele et al., 2016; Gliske et al., in press; Wang et al., 2014). It is important to note that those methods which count the number of oscillations of the filtered data may flaw when detecting broadband gamma oscillations because these are not oscillatory in nature (Lachaux et al., 2012). Finally, rigorous validation of the automated algorithm by human reviewers is mandatory. This is required because the current gold standard is the human visual expertise. Here, a common strategy is to analyze only short segments, where experts can perform a precise inspection of the raw signal to compare results with automated detectors. Nevertheless, the visual detection of HFOs requires the simultaneous display of the raw signal and the frequency representation of the same recording. This can be done by the visual inspection of the time-frequency plot altogether with the focus electrodes (Crépon et al., 2010; Worrell et al., 2004), or by the simultaneous inspection of raw and filtered EEG traces at 0.6– 0.8 s/page with high-pass filters set at 80 Hz (for visualizing ripples) and 250 Hz (for fast ripples) (Zijlmans et al., 2012). It is therefore a much more time consuming process, especially as the number of simultaneously recorded intracranial channels may reach a hundred or even more. In order to facilitate visual confirmation, an alternate strategy is to analyze segments selected during a particular state of vigilance. For instance, because the rate of occurrence of HFOs is higher during NREM sleep (Bagshaw et al., 2009; Bragin et al., 1999a, 1999b; Staba et al., 2002; von Ellenrieder et al., 2016a), many studies selected data from deepest stages of sleep. Also, stable sleep states assure the reduction of known artifacts such as of movement and muscle origin. Nevertheless, it important to note that agreement on visually detected events is also variable between experts. Here, many statistical issues must be resolved before a reliable, robust, automated method requiring minimal human interaction becomes available (Menendez de la Prida et al., 2015).

5. Time-frequency analysis of the HFOs The time-frequency transform depicts the change of the frequency components of a signal across the time (see Fig. 2). This representation has been used widely in EEG studies, and it has demonstrated to be particularly useful to the HFO analysis. The time frequency analysis is crucial for detection of broadband gamma activity (Bastin et al., 2016; Hermes et al., 2015; Jerbi et al., 2009; Manning et al., 2009; Miller et al., 2007), and because the filtering process may contaminate the data with spurious oscillations, several methods avoid the filtering by estimating the whole spectral characteristics of the signal trough the computation of the time-frequency transform (Chaibi et al., 2013, 2014; Khalilov et al., 2005; Xiang et al., 2014). In general, depending on the type of method to compute the time-frequency diagram, dissimilar characteristics will be presented for the same signal interval. Theoretically, it is not possible to achieve a time-frequency representation with high resolution for both time and frequency components; the

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increasing of the resolution in time decreases the resolution in frequency and vice versa (Cohen, 1989; Mallat, 1999). Nevertheless, some methods for computing the time-frequency plot such as the Morlet wavelet and in less manner the Morse wavelet have the special property that concentrates the time and frequency resolutions near to the theoretical minima (Lilly and Olhede, 2010, 2012). These both methods have been extensively used for the HFO analysis (Amiri et al., 2016; Axmacher et al., 2008; Bénar et al., 2010; Burnos et al., 2016; Le Van Quyen et al., 2010; Wang et al., 2013). Worrell et al. (2012) presents a comprehensive description of theory and methods to compute the time-frequency analysis of HFOs, where the main characteristics of different time-frequency transformations are compared and discussed (The Wigner-Ville Transform, the derivative of Gaussian wavelet, the Morlet Wavelet and the Morse Wavelet). Even though the use of the Morlet wavelet is widespread in the HFO analysis (Alvarado-Rojas et al., 2015; Axmacher et al., 2008; Cho et al., 2012; Le Van Quyen et al., 2006; Valderrama et al., 2012), in this work the authors conclude that the Morse wavelet transform is the most appropriate method to evaluate the temporal changes of spectral components of HFOs. However, they do not deepen into the optimal settings to implement this wavelet transform, and the required parameters have been set empirically in some studies (Amiri et al., 2016; Gadhoumi et al., 2012). The Morse Wavelet is a superfamily of analytic wavelets which are defined by the b and c factors as presented in Eqs. (3) and (4). Depending on the values of these parameters, the time and the frequency resolution will vary on the time-frequency plot, modifying the estimated spectral characteristics of the analyzed signal (Olhede and Walden, 2002). Essentially, the understanding of how these factors change the spectral characteristics of the analysis will give tools to interpret correctly the time-frequency representation. For this purpose, Lilly and Olhede (2012) describes a complete analysis of the incidence of the b and c parameters on the support, the duration, and the skewness of the frequency response of the time-frequency representation by the Morse Wavelet. Briefly, the b parameter modifies the spectral resolution of the transform, and the c parameter adjust the skewness in the frequency response of the wavelet. In this work, the authors demonstrate that the most concentrated and the most symmetric response occurs when c = 3 for b > 9. Basically, for c < 3, the frequency components above the central frequency are highlighted, while the same occurs for the frequency components below the central frequency when c > 3. Similarly, the increment of the b value represents an increasing of the frequency resolution in the wavelet transform, while lower values of b produce the increasing of the time resolution in the time-frequency plot (Lilly and Olhede, 2012).

1 Cðs; sÞ ¼ pffiffi s

  ts dt xðtÞ  w s 1

Z

1

c

Wb;c ðxÞ ¼ xb ex for x > 0

ð3Þ ð4Þ

As seen in Figs. 2 and 3, HFOs presents a bubble like shape in the time-frequency representation. When these events are analyzed in a broad band including lower frequencies, they usually appear as isolated islands in the high frequency band. This characteristic is the support in which most of the time-frequency detectors are based on (Burnos et al., 2014; Chaibi et al., 2014), but the cooccurrence of HFOs together with very sharp activity may hide the frequency components of the event of interest, reducing the possibilities that they can be detected and analyzed (Amiri et al., 2016). Nevertheless, new developments in signal processing such as the adaptive fractional spectrogram (Khan and Boashash,

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2013), the nonlinear squeezing time–frequency transform (Daubechies et al., 2011; Wang et al., 2015) and complex spectral normalization (Roehri et al., 2016) suggest the improvement of methods for the detectability of weak multicomponent events on the time-frequency transform, redirecting the path to keep looking for better strategies to identify HFOs. 6. Concluding remarks Here, we have outlined analysis protocols for HFOs. These tools have tremendous potential for the study of oscillations in multisite recordings. In particular, these computational and analytical tools are a powerful aid to generating hypothesis based on currently available data, which in turn may influence further experimental studies. Nevertheless, the automatic detection of HFOs is challenging and it is not possible to develop ‘black box’-like techniques that are a panacea to all detection problems. There is no doubt that the analysis of neuronal oscillations is still open to considerable progress. In this context, the cooperation between research centers, adopting common analysis procedures combined with the sharing of wide bandwidth data, the availability of open-access databases and the use of open-source platforms for HFO detection will help to standardize automated detection strategies of HFOs (Navarrete et al., in press). Also, all these initiatives might converge in translational efforts to create group consensus which could help to evaluate the utility of the different HFO detectors in clinical contexts by the use of open-access benchmarking tools such as been proposed for other oscillatory EEG events (O’Reilly et al., 2014; O’Reilly and Nielsen, 2015; Warby et al., 2014). In the clinical context of epilepsy, while presenting new methodological challenges, the perspectives of efficient automatic HFO detection are vast – ranging from the initial diagnostic workup of epilepsy (currently focused mainly on the visual identification of scalp IED’s (Noachtar and Rémi, 2009)), through noninvasive focus localization (which may be of particular value in focal epilepsy patients with negative neuroimaging results) up to longterm monitoring of antiepileptic drug treatment (HFO rates have been shown to follow changes in medication dosing (Zelmann et al., 2009)). The clinical scope of HFO applicability may someday even extend beyond the field of epilepsy such has been suggested for schizophrenia (Tekell et al., 2004; Uhlhaas and Singer, 2010, 2013) and Parkinson disease (Wang et al., 2014). Nevertheless, from the strict perspective of evidence-based medicine, the overall level of evidence for introducing HFOs to routine practice still remains insufficient, mostly due to the lack of randomized controlled clinical trials (Gloss et al., 2014). References Aivar, P., Valero, M., Bellistri, E., Menendez de la Prida, L., 2014. Extracellular calcium controls the expression of two different forms of ripple-like hippocampal oscillations. J. Neurosci. 34, 2989–3004. http://dx.doi.org/ 10.1523/JNEUROSCI.2826-13.2014. Akiyama, T., McCoy, B., Go, C.Y., Ochi, A., Elliott, I.M., Akiyama, M., Donner, E.J., Weiss, S.K., Snead, O.C., Rutka, J.T., Drake, J.M., Otsubo, H., 2011. Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy. Epilepsia 52, 1802–1811. Alvarado-Rojas, C., Huberfeld, G., Baulac, M., Clemenceau, S., Charpier, S., Miles, R., de la Prida, L.M., Le Van Quyen, M., 2015. Different mechanisms of ripple-like oscillations in the human epileptic subiculum. Ann. Neurol. 77, 281–290. http:// dx.doi.org/10.1002/ana.24324. Amiri, M., Lina, J.-M., Pizzo, F., Gotman, J., 2016. High frequency oscillations and spikes: separating real HFOs from false oscillations. Clin. Neurophysiol. 127, 187–196. http://dx.doi.org/10.1016/j.clinph.2015.04.290. Andrade-Valenca, L.P., Dubeau, F., Mari, F., Zelmann, R., Gotman, J., 2011. Interictal scalp fast oscillations as a marker of the seizure onset zone. Neurology 77, 524– 531. Axmacher, N., Elger, C.E., Fell, J., 2008. Ripples in the medial temporal lobe are relevant for human memory consolidation. Brain 131, 1806–1817. http://dx.doi. org/10.1093/brain/awn103.

Bagshaw, A.P., Jacobs, J., LeVan, P., Dubeau, F., Gotman, J., 2009. Effect of sleep stage on interictal high-frequency oscillations recorded from depth macroelectrodes in patients with focal epilepsy. Epilepsia 50, 617–628. http://dx.doi.org/ 10.1111/j.1528-1167.2008.01784.x. ˇ mejla, R., Kršek, P., Marusicˇ, P., Jiruška, P., Balach, J., Havel, T., Jancˇa, R., Jezˇdík, P., C 2015. 53. Methods of high frequency oscillations detection: advantages and disadvantages. Clin. Neurophysiol. 126, e49. http://dx.doi.org/10.1016/ j.clinph.2014.10.212. Bastin, J., Deman, P., David, O., Gueguen, M., Benis, D., Minotti, L., Hoffman, D., Combrisson, E., Kujala, J., Perrone-Bertolotti, M., Kahane, P., Lachaux, J.-P., Jerbi, K., 2016. Direct recordings from human anterior insula reveal its leading role within the error-monitoring network. Cereb. Cortex 1–13. http://dx.doi.org/ 10.1093/cercor/bhv352. Bénar, C.G., Chauvière, L., Bartolomei, F., Wendling, F., 2010. Pitfalls of high-pass filtering for detecting epileptic oscillations: a technical note on ‘‘false” ripples. Clin. Neurophysiol. 121, 301–310. http://dx.doi.org/10.1016/j.clinph. 2009.10.019. Birot, G., Kachenoura, A., Albera, L., Bénar, C., Wendling, F., 2013. Automatic detection of fast ripples. J. Neurosci. Methods 213, 236–249. http://dx.doi.org/ 10.1016/j.jneumeth.2012.12.013. Blanco, J., Stead, M., Krieger, A., Viventi, J., Marsh, R., Lee, K.H., Worrell, G.A., Litt1, B., 2010. Unsupervised classification of high-frequency oscillations in human neocortical epilepsy and control patients. J. Neurophysiol. 2900–2912. http:// dx.doi.org/10.1152/jn.01082.2009. Bragin, A., Engel, J., Wilson, C.L., Fried, I., Buzsáki, G., 1999a. High-frequency oscillations in human brain. Hippocampus 9, 137–142. http://dx.doi.org/ 10.1002/(SICI)1098-1063(1999)9:23.0.CO;2-0. Bragin, A., Engel, J., Wilson, C.L., Fried, I., Mathern, G.W., 1999b. Hippocampal and entorhinal cortex high-frequency oscillations (100–500 Hz) in human epileptic brain and in kainic acid-treated rats with chronic seizures. Epilepsia 40, 127– 137. http://dx.doi.org/10.1111/j.1528-1157.1999.tb02065.x. Bragin, A., Mody, I., Wilson, C.L., Engel, J., 2002. Local generation of fast ripples in epileptic brain. J. Neurosci. 22, 2012–2021, pii: 22/5/2012. Burnos, S., Frauscher, B., Zelmann, R., Haegelen, C., Sarnthein, J., Gotman, J., 2016. The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone. Clin. Neurophysiol. 127, 2140–2148. http://dx.doi.org/10.1016/j.clinph.2016.01.002. Burnos, S., Hilfiker, P., Sürücü, O., Scholkmann, F., Krayenbühl, N., Grunwald, T., Sarnthein, J., 2014. Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis. PLoS ONE 9, e94381. http://dx. doi.org/10.1371/journal.pone.0094381. Buzsaki, G., Draguhn, A., 2004. Neuronal oscillations in cortical networks. Science (80-) 304, 1926–1929. http://dx.doi.org/10.1126/science.1099745. Buzsáki, G., Horváth, Z., Urioste, R., Hetke, J., Wise, K., 1992. High-frequency network oscillation in the hippocampus. Science 256, 1025–1027. http://dx.doi. org/10.1126/science.1589772. Buzsáki, G., Lopes da Silva, F., 2012. High frequency oscillations in the intact brain. Prog. Neurobiol. 98, 241–249. http://dx.doi.org/10.1016/j.pneurobio. 2012.02.004. Canolty, R.T., Edwards, E., Dalal, S.S., Soltani, M., Nagarajan, S.S., Kirsch, H.E., Berger, M.S., Barbaro, N.M., Knight, R.T., 2006. High gamma power is phase-locked to theta oscillations in human neocortex. Science 313, 1626–1628. http://dx.doi. org/10.1126/science.1128115. Carr, M.F., Karlsson, M.P., Frank, L.M., 2012. Transient slow gamma synchrony underlies hippocampal memory replay. Neuron 75, 700–713. http://dx.doi.org/ 10.1016/j.neuron.2012.06.014. Chaibi, S., Lajnef, T., Sakka, Z., Samet, M., Kachouri, A., 2014. A reliable approach to distinguish between transient with and without HFOs using TQWT and MCA. J. Neurosci. Methods 232, 36–46. http://dx.doi.org/10.1016/j. jneumeth.2014.04.025. Chaibi, S., Lajnef, T., Sakka, Z., Samet, M., Kachouri, A., 2013. A comparison of methods for detection of high frequency oscillations (HFOs) in human intacerberal EEG recordings. Am. J. Ellipsis 3, 25–34. http://dx.doi.org/ 10.5923/j.ajsp.20130302.02. Chaitanya, G., Sinha, S., Narayanan, M., Satishchandra, P., 2015. Scalp high frequency oscillations (HFOs) in absence epilepsy: an independent component analysis (ICA) based approach. Epilepsy Res. 115, 133–140. http://dx.doi.org/10.1016/j. eplepsyres.2015.06.008. Cho, J.R., Joo, E.Y., Koo, D.L., Hong, S.C., Hong, S.B., 2012. Clinical utility of interictal high-frequency oscillations recorded with subdural macroelectrodes in partial epilepsy. J. Clin. Neurol. 8, 22–34. http://dx.doi.org/10.3988/jcn.2012.8.1.22. Cho, J.R., Koo, D.L., Joo, E.Y., Seo, D.W., Hong, S.C., Jiruska, P., Hong, S.B., 2014. Resection of individually identified high-rate high-frequency oscillations region is associated with favorable outcome in neocortical epilepsy. Epilepsia 55, 1872–1883. Chrobak, J.J., Buzsáki, G., 1998. Gamma oscillations in the entorhinal cortex of the freely behaving rat. J. Neurosci. 18, 388–398. 0270-6474/97/180388-11$05.00/0. Chrobak, J.J., Buzsáki, G., 1996. High-frequency oscillations in the output networks of the hippocampal-entorhinal axis of the freely behaving rat. J. Neurosci. 16, 3056–3066. Clemens, Z., Mölle, M., Eross, L., Barsi, P., Halász, P., Born, J., Molle, M., Eross, L., Barsi, P., Halasz, P., Born, J., 2007. Temporal coupling of parahippocampal ripples, sleep spindles and slow oscillations in humans. Brain 130, 2868–2878. http:// dx.doi.org/10.1093/brain/awm146. Cohen, L., 1989. Time-frequency distributions – a review. Proc. IEEE 77, 941–981. http://dx.doi.org/10.1109/5.30749.

M. Navarrete et al. / Journal of Physiology - Paris 110 (2016) 316–326 Colgin, L.L., Denninger, T., Fyhn, M., Hafting, T., Bonnevie, T., Jensen, O., Moser, M.-B., Moser, E.I., 2009. Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462, 353–357. http://dx.doi.org/10.1038/ nature08573. Colgin, L.L., Moser, E.I., 2010. Gamma oscillations in the hippocampus. Physiology 25, 319–329. http://dx.doi.org/10.1152/physiol.00021.2010. Collura, T.F., Lüders, H., Burgess, R.C., 1990. EEG mapping for surgery of epilepsy. Brain Topogr. 3, 65–77. http://dx.doi.org/10.1007/BF01128863. Cooper, R., Winter, A., Crow, H., Walter, W.G., 1965. Comparison of subcortical, cortical and scalp activity using chronically indwelling electrodes in man. Electroencephalogr. Clin. Neurophysiol. 18, 217–228. Corlier, J., Rimsky-Robert, D., Valderrama, M., Lehongre, K., Adam, C., Clémenceau, S., Charpier, S., Bastin, J., Kahane, P., Lachaux, J.-P., Navarro, V., Le Van Quyen, M., 2016. Self-induced intracerebral gamma oscillations in the human cortex. Brain 139, 3084–3091. http://dx.doi.org/10.1093/brain/aww246. Crépon, B., Navarro, V., Hasboun, D., Clemenceau, S., Martinerie, J., Baulac, M., Adam, C., Le Van Quyen, M., 2010. Mapping interictal oscillations greater than 200 Hz recorded with intracranial macroelectrodes in human epilepsy. Brain 133, 33– 45. http://dx.doi.org/10.1093/brain/awp277. Daubechies, I., Lu, J., Wu, H.T., 2011. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal. 30, 243– 261. http://dx.doi.org/10.1016/j.acha.2010.08.002. Destexhe, a., Contreras, D., Steriade, M., 1999. Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. J. Neurosci. 19, 4595–4608. Dümpelmann, M., Jacobs, J., Kerber, K., Schulze-Bonhage, A., 2012. Automatic 80– 250 Hz ‘‘ripple” high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network. Clin. Neurophysiol. 123, 1721–1731. http://dx.doi.org/10.1016/j.clinph.2012.02.072. Dümpelmann, M., Jacobs, J., Schulze-Bonhage, A., 2015. Temporal and spatial characteristics of high frequency oscillations as a new biomarker in epilepsy. Epilepsia 56, 197–206. http://dx.doi.org/10.1111/epi.12844. Engel, J., Lopes da Silva, F., 2012. High-frequency oscillations – where we are and where we need to go. Prog. Neurobiol. 98, 316–318. http://dx.doi.org/10.1016/j. pneurobio.2012.02.001. Esteller, R., Echauz, J., Tcheng, T., Litt, B., Pless, B., 2001. Line length: an efficient feature for seizure onset detection. In: 2001 Conf. Proc. 23rd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2. Fedele, T., van’t Klooster, M., Burnos, S., Zweiphenning, W., van Klink, N., Leijten, F., Zijlmans, M., Sarnthein, J., 2016. Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome. Clin. Neurophysiol. 127, 3066–3074. http://dx.doi.org/10.1016/j.clinph.2016.06.009. Foffani, G., Uzcategui, Y.G., Gal, B., Menendez de la Prida, L., 2007. Reduced spiketiming reliability correlates with the emergence of fast ripples in the rat epileptic hippocampus. Neuron 55, 930–941. http://dx.doi.org/10.1016/j. neuron.2007.07.040. Fries, P., 2009. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu. Rev. Neurosci. 32, 209–224. http://dx.doi.org/ 10.1146/annurev.neuro.051508.135603. Fries, P., Reynolds, J.H., Rorie, A.E., Desimone, R., 2001. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560– 1563. http://dx.doi.org/10.1126/science.291.5508.1560. Fujiwara, H., Greiner, H.M., Lee, K.H., Holland-Bouley, K.D., Seo, J.H., Arthur, T., Mangano, F.T., Leach, J.L., Rose, D.F., 2012. Resection of ictal high-frequency oscillations leads to favorable surgical outcome in pediatric epilepsy. Epilepsia 53, 1607–1617. Gadhoumi, K., Lina, J.-M., Gotman, J., 2012. Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clin. Neurophysiol. 123, 1906–1916. http://dx.doi.org/ 10.1016/j.clinph.2012.03.001. Gardner, A.B., Worrell, G.A., Marsh, E., Dlugos, D., Litt, B., 2007. Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings. Clin. Neurophysiol. 118, 1134–1143. http://dx.doi.org/10.1016/ j.clinph.2006.12.019. Gliske, S.V., Irwin, Z.T., Davis, K.a., Sahaya, K., Chestek, C., Stacey, W.C., in press. Universal automated high frequency oscillation detector for real-time, long term EEG. Clin. Neurophysiol. http://dx.doi.org/10.1016/j.clinph.2015.07.016 (in press). Gloss, D., Nolan, S.J., Staba, R., 2014. The role of high-frequency oscillations in epilepsy surgery planning. Cochrane Database Syst. Rev. 1, CD010235. Haufler, D., Pare, D., Paré, D., 2014. High-frequency oscillations are prominent in the extended amygdala. J. Neurophysiol. 112, 110–119. http://dx.doi.org/10.1152/ jn.00107.2014. Hermes, D., Miller, K.J., Wandell, B.A., Winawer, J., 2015. Stimulus dependence of gamma oscillations in human visual cortex. Cereb. Cortex 25, 2951–2959. http://dx.doi.org/10.1093/cercor/bhu091. Höller, Y., Kutil, R., Klaffenböck, L., Thomschewski, A., Höller, P.M., Bathke, A.C., Jacobs, J., Taylor, A.C., Nardone, R., Trinka, E., 2015. High-frequency oscillations in epilepsy and surgical outcome. A meta-analysis. Front. Hum. Neurosci. 9, 1–14. Jacobs, J., LeVan, P., Chander, R., Hall, J., Dubeau, F., Gotman, J., 2008. Interictal highfrequency oscillations (80–500 Hz) are an indicator of seizure onset areas independent of spikes in the human epileptic brain. Epilepsia 49, 1893–1907. http://dx.doi.org/10.1111/j.1528-1167.2008.01656.x. Jacobs, J., Levan, P., Châtillon, C.-E., Olivier, A., Dubeau, F., Gotman, J., 2009. High frequency oscillations in intracranial EEGs mark epileptogenicity rather than lesion type. Brain 132, 1022–1037. http://dx.doi.org/10.1093/brain/awn351.

325

Jacobs, J., Staba, R., Asano, E., Otsubo, H., Wu, J.Y., Zijlmans, M., Mohamed, I., Kahane, P., Dubeau, F., Navarro, V., Gotman, J., 2012. High-frequency oscillations (HFOs) in clinical epilepsy. Prog. Neurobiol. 98, 302–315. http://dx.doi.org/10.1016/j. pneurobio.2012.03.001. Jacobs, J., Zijlmans, M., Zelmann, R., Chatillon, C.É., Hall, J., Olivier, A., Dubeau, F., Gotman, J., 2010. High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann. Neurol. 67, 209–220. Jefferys, J.G.R., Menendez de la Prida, L., Wendling, F., Bragin, A., Avoli, M., Timofeev, I., Lopes da Silva, F.H., 2012. Mechanisms of physiological and epileptic HFO generation. Prog. Neurobiol. 98, 250–264. http://dx.doi.org/10.1016/j. pneurobio.2012.02.005. Jerbi, K., Ossandón, T., Hamamé, C.M., Senova, S., Dalal, S.S., Jung, J., Minotti, L., Bertrand, O., Berthoz, A., Kahane, P., Lachaux, J.P., 2009. Task-related gammaband dynamics from an intracerebral perspective: review and implications for surface EEG and MEG. Hum. Brain Mapp. 30, 1758–1771. http://dx.doi.org/ 10.1002/hbm.20750. Jirsch, J.D., Urrestarazu, E., LeVan, P., Olivier, A., Dubeau, F., Gotman, J., 2006. Highfrequency oscillations during human focal seizures. Brain 129, 1593–1608. Kerber, K., Dümpelmann, M., Schelter, B., Le Van, P., Korinthenberg, R., SchulzeBonhage, A., Jacobs, J., 2014. Differentiation of specific ripple patterns helps to identify epileptogenic areas for surgical procedures. Clin. Neurophysiol. 125, 1339–1345. http://dx.doi.org/10.1016/j.clinph.2013.11.030. Khalilov, I., Le Van Quyen, M., Gozlan, H., Ben-Ari, Y., 2005. Epileptogenic actions of GABA and fast oscillations in the developing hippocampus. Neuron 48, 787– 796. http://dx.doi.org/10.1016/j.neuron.2005.09.026. Khan, N.A., Boashash, B., 2013. Instantaneous frequency estimation of multicomponent nonstationary signals using multiview time-frequency distributions based on the adaptive fractional spectrogram. IEEE Signal Process. Lett. 20, 157–160. http://dx.doi.org/10.1109/LSP.2012.2236088. Lachaux, J.-P., Axmacher, N., Mormann, F., Halgren, E., Crone, N.E., 2012. Highfrequency neural activity and human cognition: past, present and possible future of intracranial EEG research. Prog. Neurobiol. 98, 279–301. http://dx.doi. org/10.1016/j.pneurobio.2012.06.008. Lachaux, J.-P., George, N., Tallon-Baudry, C., Martinerie, J., Hugueville, L., Minotti, L., Kahane, P., Renault, B., 2005. The many faces of the gamma band response to complex visual stimuli. Neuroimage 25, 491–501. http://dx.doi.org/10.1016/j. neuroimage.2004.11.052. Lantz, G., Grave de Peralta, R., Spinelli, L., Seeck, M., Michel, C.M., 2003. Epileptic source localization with high density EEG: how many electrodes are needed? Clin. Neurophysiol. 114, 63–69. Le Van Quyen, M., 2012. This special issue of Progress in Neurobiology is dedicated to the meeting held at the Montreal Neurological Institute, Canada. Prog. Neurobiol. 98, 239–240. Le Van Quyen, M., Khalilov, I., Ben-Ari, Y., 2006. The dark side of high-frequency oscillations in the developing brain. Trends Neurosci. 29, 419–427. http://dx. doi.org/10.1016/j.tins.2006.06.001. Le Van Quyen, M., Staba, R., Bragin, A., Dickson, C., Valderrama, M., Fried, I., Engel, J., 2010. Large-scale microelectrode recordings of high-frequency gamma oscillations in human cortex during sleep. J. Neurosci. 30, 7770–7782. http:// dx.doi.org/10.1523/JNEUROSCI.5049-09.2010. Lee, A.K., Wilson, M.A., 2002. Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36, 1183–1194. http://dx.doi.org/10.1016/ S0896-6273(02)01096-6. Lilly, J.M., Olhede, S.C., 2012. Generalized Morse wavelets as a superfamily of analytic wavelets. IEEE Trans. Signal Process. 60, 6036–6041. http://dx.doi.org/ 10.1109/TSP.2012.2210890. Lilly, J.M., Olhede, S.C., 2010. On the analytic wavelet transform. IEEE Trans. Inf. Theory 56, 4135–4156. http://dx.doi.org/10.1109/TIT.2010.2050935. Litt, B., Esteller, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., Pennell, P., Epstein, C., Bakay, R., Dichter, M., Vachtsevanos, G., 2001. Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. Neuron 30, 51–64. López-Cuevas, A., Castillo-Toledo, B., Medina-Ceja, L., Ventura-Mejía, C., Pardo-Peña, K., 2013. An algorithm for on-line detection of high frequency oscillations related to epilepsy. Comput. Methods Programs Biomed. 110, 354–360. http:// dx.doi.org/10.1016/j.cmpb.2013.01.014. Mallat, S., 1999. A Wavelet Tour of Signal Processing. Elsevier. Manning, J.R., Jacobs, J., Fried, I., Kahana, M.J., 2009. Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. J. Neurosci. 29, 13613–13620. http://dx.doi.org/10.1523/JNEUROSCI.204109.2009. Matsumoto, A., Brinkmann, B.H., Stead, S.M., Matsumoto, J., Kucewicz, M.T., Marsh, W.R., Meyer, F., Worrell, G., 2013. Pathological and physiological highfrequency oscillations in focal human epilepsy. J. Neurophysiol. 110, 1958– 1964. http://dx.doi.org/10.1152/jn.00341.2013. Melani, F., Zelmann, R., Dubeau, F., Gotman, J., 2013. Occurrence of scalp-fast oscillations among patients with different spiking rate and their role as epileptogenicity marker. Epilepsy Res. 106, 345–356. Menendez de la Prida, L., Staba, R.J., Dian, J.A., 2015. Conundrums of high-frequency oscillations (80–800 Hz) in the epileptic brain. J. Clin. Neurophysiol. 32, 207– 219. http://dx.doi.org/10.1097/WNP.0000000000000150. Miao, A., Xiang, J., Tang, L., Ge, H., Liu, H., Wu, T., Chen, Q., Hu, Z., Lu, X., Wang, X., 2014. Using ictal high-frequency oscillations (80–500 Hz) to localize seizure onset zones in childhood absence epilepsy: a MEG study. Neurosci. Lett. 566, 21–26. http://dx.doi.org/10.1016/j.neulet.2014.02.038.

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M. Navarrete et al. / Journal of Physiology - Paris 110 (2016) 316–326

Miller, K.J., Leuthardt, E.C., Schalk, G., Rao, R.P.N., Anderson, N.R., Moran, D.W., Miller, J.W., Ojemann, J.G., 2007. Spectral changes in cortical surface potentials during motor movement. J. Neurosci. 27, 2424–2432. http://dx.doi.org/10.1523/ JNEUROSCI.3886-06.2007. Montgomery, S.M., Buzsáki, G., 2007. Gamma oscillations dynamically couple hippocampal CA3 and CA1 regions during memory task performance. Proc. Natl. Acad. Sci. U.S.A. 104, 14495–14500. http://dx.doi.org/10.1073/pnas.0701826104. Navarrete, M., Alvarado-Rojas, C., Le Van Quyen, M., Valderrama, M., in press. RIPPLELAB: a comprehensive application for the detection, analysis and classification of high frequency oscillations in electroencephalographic signals. PLoS ONE (in press). Nikolic´, D., Fries, P., Singer, W., 2013. Gamma oscillations: precise temporal coordination without a metronome. Trends Cogn. Sci. 17, 54–55. http://dx.doi. org/10.1016/j.tics.2012.12.003. Nissen, I.A., van Klink, N.E.C., Zijlmans, M., Stam, C.J., Hillebrand, A., 2016. Brain areas with epileptic high frequency oscillations are functionally isolated in MEG virtual electrode networks. Clin. Neurophysiol. 127, 2581–2591. http://dx.doi. org/10.1016/j.clinph.2016.04.013. Noachtar, S., Rémi, J., 2009. The role of EEG in epilepsy: a critical review. Epilepsy Behav. O’Keefe, J., Nadel, L., 1978. The Hippocampus as a Cognitive Map. Oxford University Press. O’Reilly, C., Gosselin, N., Carrier, J., Nielsen, T., 2014. Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research. J. Sleep Res. 23, 628–635. http://dx.doi.org/10.1111/jsr.12169. O’Reilly, C., Nielsen, T., 2015. Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools. Front. Hum. Neurosci. 9, 353. http://dx.doi.org/10.3389/fnhum.2015.00353. Olhede, S.C., Walden, A.T., 2002. Generalized Morse wavelets. IEEE Trans. Signal Process. 50, 2661–2670. http://dx.doi.org/10.1109/TSP.2002.804066. Papadelis, C., Poghosyan, V., Fenwick, P.B.C., Ioannides, A.A., 2009. MEG’s ability to localise accurately weak transient neural sources. Clin. Neurophysiol. 120, 1958–1970. http://dx.doi.org/10.1016/j.clinph.2009.08.018. Pizzo, F., Frauscher, B., Ferrari-Marinho, T., Amiri, M., Dubeau, F., Gotman, J., 2016. Detectability of fast ripples (>250 Hz) on the scalp EEG: a proof-of-principle study with subdermal electrodes. Brain Topogr. 29, 358–367. http://dx.doi.org/ 10.1007/s10548-016-0481-7. Rampp, S., Kaltenhäuser, M., Weigel, D., Buchfelder, M., Blümcke, I., Dörfler, A., Stefan, H., 2010. MEG correlates of epileptic high gamma oscillations in invasive EEG. Epilepsia 51, 1638–1642. http://dx.doi.org/10.1111/j.1528-1167.2010.02579.x. Ray, S., Maunsell, J.H.R., 2011. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9. http://dx.doi.org/10.1371/ journal.pbio.1000610. Ren, L., Kucewicz, M.T., Cimbalnik, J., Matsumoto, J.Y., Brinkmann, B.H., Hu, W., Marsh, W.R., Meyer, F.B., Stead, S.M., Worrell, G.A., 2015. Gamma oscillations precede interictal epileptiform spikes in the seizure onset zone. Neurology 84, 602–608. http://dx.doi.org/10.1212/WNL.0000000000001234. Roehri, N., Lina, J., Mosher, J.C., Bartolomei, F., Roehri, N., Lina, J., Mosher, J.C., Bartolomei, F., 2016. Time-frequency strategies for increasing high frequency oscillation detectability in intracerebral. IEEE Trans. Biomed. Eng. 2, 1–12. http://dx.doi.org/10.1109/TBME.2016.2556425. Rosso, O., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schürmann, M., Basßar, E., 2001. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J. Neurosci. Methods 105, 65–75. Sakuma, K., Sekihara, K., Hashimoto, I., 1999. Neural source estimation from a timefrequency component of somatic evoked high-frequency magnetic oscillations to posterior tibial nerve stimulation. Clin. Neurophysiol. 110, 1585–1588. Salami, P., Lévesque, M., Gotman, J., Avoli, M., 2012. A comparison between automated detection methods of high-frequency oscillations (80–500 Hz) during seizures. J. Neurosci. Methods 211, 265–271. http://dx.doi.org/10.1016/j. jneumeth.2012.09.003. Schimicek, P., Zeitlhofer, J., Anderer, P., Saletu, B., 1994. Automatic sleep-spindle detection procedure: aspects of reliability and validity. Clin. EEG Neurosci. 25, 26–29. http://dx.doi.org/10.1177/155005949402500108. Singer, W., Gray, C.M., 1995. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. http://dx.doi.org/10.1146/annurev. ne.18.030195.003011. Staba, R.J., Wilson, C.L., Bragin, A., Fried, I., Engel, J., 2002. Quantitative analysis of high-frequency oscillations (80–500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. J. Neurophysiol. 88, 1743–1752. http:// dx.doi.org/10.1152/jn.00322.2002. Staba, R.J., Wilson, C.L., Bragin, A., Jhung, D., Fried, I., Engel, J., 2004. High-frequency oscillations recorded in human medial temporal lobe during sleep. Ann. Neurol. 56, 108–115. http://dx.doi.org/10.1002/ana.20164. Tallon-Baudry, C., Bertrand, O., 1999. Oscillatory gamma activity in humans and its role in object representation. Trends Cogn. Sci. 3, 151–162. http://dx.doi.org/ 10.1016/S1364-6613(99)01299-1. Tao, J.X., Ray, A., Hawes-Ebersole, S., Ebersole, J.S., 2005. Intracranial EEG substrates of scalp EEG interictal spikes. Epilepsia. Tekell, J.L., Hoffmann, R., Hendrickse, W., Greene, R.W., Rush, A.J., Armitage, R., 2004. High Frequency EEG Activity during Sleep: Characteristics in Schizophrenia and Depression 36. Tenney, J.R., Fujiwara, H., Horn, P.S., Vannest, J., Xiang, J., Glauser, T.A., Rose, D.F., 2014. Low- and high-frequency oscillations reveal distinct absence seizure networks. Ann. Neurol. 76, 558–567. http://dx.doi.org/10.1002/ana.24231.

Uhlhaas, P.J., Singer, W., 2013. High-frequency oscillations and the neurobiology of schizophrenia. Dialog. Clin. Neurosci. 15, 301–313. Uhlhaas, P.J., Singer, W., 2012. Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctional large-scale networks. Neuron 75, 963–980. http://dx.doi.org/10.1016/j.neuron.2012.09.004. Uhlhaas, P.J., Singer, W., 2010. Abnormal neural oscillations and synchrony in schizophrenia. Nat. Rev. Neurosci. 11, 100–113. http://dx.doi.org/10.1038/ nrn2774. Valderrama, M., Crépon, B., Botella-Soler, V., Martinerie, J., Hasboun, D., AlvaradoRojas, C., Baulac, M., Adam, C., Navarro, V., Le Van Quyen, M., 2012. Human gamma oscillations during slow wave sleep. PLoS ONE 7, e33477. http://dx.doi. org/10.1371/journal.pone.0033477. van Klink, N., Hillebrand, A., Zijlmans, M., 2016. Identification of epileptic high frequency oscillations in the time domain by using MEG beamformer-based virtual sensors. Clin. Neurophysiol. 127, 197–208. http://dx.doi.org/10.1016/ j.clinph.2015.06.008. von Ellenrieder, N., Andrade-Valença, L.P., Dubeau, F., Gotman, J., 2012. Automatic detection of fast oscillations (40–200 Hz) in scalp EEG recordings. Clin. Neurophysiol. 123, 670–680. http://dx.doi.org/10.1016/j.clinph.2011.07.050. von Ellenrieder, N., Frauscher, B., Dubeau, F., Gotman, J., 2016a. Interaction with slow waves during sleep improves discrimination of physiologic and pathologic high-frequency oscillations (80–500 Hz). Epilepsia 869–878. http://dx.doi.org/ 10.1111/epi.13380. von Ellenrieder, N., Pellegrino, G., Hedrich, T., Gotman, J., Lina, J.-M., Grova, C., Kobayashi, E., 2016b. Detection and magnetic source imaging of fast oscillations (40–160 Hz) recorded with magnetoencephalography in focal epilepsy patients. Brain Topogr. 29, 218–231. http://dx.doi.org/10.1007/s10548-016-0471-9. Wang, C., Brunton, E., Haghgooie, S., Cassells, K., Lowery, A., Rajan, R., 2013. Characteristics of electrode impedance and stimulation efficacy of a chronic cortical implant using novel annulus electrodes in rat motor cortex. J. Neural Eng. 10, 46010. http://dx.doi.org/10.1088/1741-2560/10/4/046010. Wang, J., Hirschmann, J., Elben, S., Hartmann, C.J., Vesper, J., Wojtecki, L., Schnitzler, A., 2014. High-frequency oscillations in Parkinson’s disease: spatial distribution and clinical relevance. Mov. Disord. 29, 1265–1272. http://dx.doi.org/10.1002/ mds.25962. Wang, S., Chen, X., Wang, Y., Cai, G., Ding, B., Zhang, X., 2015. Nonlinear squeezing time–frequency transform for weak signal detection. Signal Process. 113, 195– 210. http://dx.doi.org/10.1016/j.sigpro.2015.01.022. Warby, S.C., Wendt, S.L., Welinder, P., Munk, E.G.S., Carrillo, O., Sorensen, H.B.D., Jennum, P., Peppard, P.E., Perona, P., Mignot, E., 2014. Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nat. Methods 11, 385–392. http://dx.doi.org/10.1038/ nmeth.2855. Widmann, A., Schröger, E., Maess, B., 2014. Digital filter design for electrophysiological data – a practical approach. J. Neurosci. Methods 250, 34–46. http://dx.doi.org/10.1016/j.jneumeth.2014.08.002. Wilson, M., McNaughton, B., 1994. Reactivation of hippocampal ensemble memories during sleep. Science (80-) 265, 676–679. http://dx.doi.org/ 10.1126/science.8036517. Wong, P.K.H., 1996. Quantitative analysis of epileptic discharges. Brain Topogr. 8, 209–214. http://dx.doi.org/10.1007/BF01184771. Worrell, G.A.A., Jerbi, K., Kobayashi, K., Lina, J.M.M., Zelmann, R., Le Van Quyen, M., 2012. Recording and analysis techniques for high-frequency oscillations. Prog. Neurobiol. 98, 265–278. http://dx.doi.org/10.1016/j.pneurobio.2012.02.006. Worrell, G.A., Parish, L., Cranstoun, S.D., Jonas, R., Baltuch, G., Litt, B., 2004. Highfrequency oscillations and seizure generation in neocortical epilepsy. Brain 127, 1496–1506. http://dx.doi.org/10.1093/brain/awh149. Xiang, J., Liu, Y., Wang, Y., Kirtman, E.G., Kotecha, R., Chen, Y., Huo, X., Fujiwara, H., Hemasilpin, N., Lee, K., Mangano, F.T., Leach, J., Jones, B., Degrauw, T., Rose, D., 2009. Frequency and spatial characteristics of high-frequency neuromagnetic signals in childhood epilepsy. Epileptic Disord. 11, 113–125. Xiang, J., Luo, Q., Kotecha, R., Korman, A., Zhang, F., Luo, H., Fujiwara, H., Hemasilpin, N., Rose, D.F., 2014. Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals. Front. Neuroinform. 8, 57. http://dx. doi.org/10.3389/fninf.2014.00057. Zelmann, R., Lina, J.M., Schulze-Bonhage, A., Gotman, J., Jacobs, J., 2014. Scalp EEG is not a blur: it can see high frequency oscillations although their generators are small. Brain Topogr. 27, 683–704. Zelmann, R., Mari, F., Jacobs, J., Zijlmans, M., Chander, R., Gotman, J., 2010. Automatic detector of high frequency oscillations for human recordings with macroelectrodes. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010, 2329–2333. http:// dx.doi.org/10.1109/IEMBS.2010.5627464. Zelmann, R., Mari, F., Jacobs, J., Zijlmans, M., Dubeau, F., Gotman, J., 2012. A comparison between detectors of high frequency oscillations. Clin. Neurophysiol. 123, 106–116. http://dx.doi.org/10.1016/j.clinph.2011.06.006. Zelmann, R., Zijlmans, M., Jacobs, J., Châtillon, C.-E., Gotman, J., 2009. Improving the identification of high frequency oscillations. Clin. Neurophysiol. 120, 1457– 1464. http://dx.doi.org/10.1016/j.clinph.2009.05.029. Zijlmans, M., Jacobs, J., Kahn, Y.U., Zelmann, R., Dubeau, F., Gotman, J., 2011. Ictal and interictal high frequency oscillations in patients with focal epilepsy. Clin. Neurophysiol. 122, 664–671. http://dx.doi.org/10.1016/j.clinph.2010.09.021. Zijlmans, M., Jiruska, P., Zelmann, R., Leijten, F.S.S., Jefferys, J.G.R., Gotman, J., 2012. High-frequency oscillations as a new biomarker in epilepsy. Ann. Neurol. 71, 169–178.