20th International Conference of Computer and Information Technology (ICCIT), 22-24 December, 2017
Rhythmic Component Retrieval from EOG Artifact of Electroencephalography Signals Sania Zahan1, Md. Ashaduzzaman Milu1 and Md. Khademul Islam Molla2 1
Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh Email:
[email protected] 2 Department of Computer Science and Engineering, University of Rajshahi, Bangladesh Email:
[email protected]
Abstract— Electroencephalography (EEG) signal has been widely used to recognize the intention and cognition in brain computer interface (BCI) applications. Non-Cerebral signals like electro-oculogram (EOG) artifacts with higher energy suppress important low energy EEG components. It is required to completely remove these artifacts without loss of EEG data. This paper presents multivariate empirical mode decomposition (MEMD) based filtering approach to suppress the unwanted artifacts. The multichannel EEG signal is decomposed into a finite number of band limited signals called intrinsic mode functions (IMFs). A subband thresholding is implemented to separate artifacts using the IMFs with fractional Gaussian noise (fGn) as reference signal. Thus separated artifact includes some EEG components. Bandpass filtering is implemented to extract the EEG signal from artifact obtained by energy based thresholding. The experimental results conducted with real EEG signals show the effectiveness of the proposed method.
applied to separate the EOG interference. It is observed that EOG separation is not perfect and it is difficult to identify only the EOG signal [6]. Though MEMD can efficiently separate signals but information is still lost in the threshold process.
Keywords—Bandpass filter, electroencephalography (EEG), electro-oculogram (EOG), empirical mode decomposition (EMD).
In this paper, zero phase bandpass filter is used to identify if any EEG components in the separated EOG signal using MEMD based subband thresholding approach. We have used noise-assisted multivariate empirical mode decomposition (NA-MEMD) [9] to decompose EEG signal and energy based threshold to identify frequency trends of EOG artifacts. A 4th order Butterworth filter is used to extract any remaining rhythmic components. Finally the previously separated EEG and the extracted components are added to reconstruct clean EEG signal with reduced data loss.
I. INTRODUCTION Brain computer interfacing is a powerful communication tool between human and machine. It does not require any external devices or muscle intervention to issue commands and complete the interaction [1]. Intentions can be identified from EEG signal and then translated into computer command to carry out the instruction. Some promising areas of BCI are user-state monitoring, device control, gaming, cognitive improvement, safety and security etc. EEG signal can also be used to diagnose various types of diseases like epilepsy, sleep disorder, brain death, Alzheimer etc. [2]. But artifact in EEG due to eyeball movement and blinking called electro-oculogram (EOG) is present all over the signal. Auditory and mental arithmetic tasks with the eyes closed, leads to strong alpha waves, which are suppressed when the eyes are opened [2]. The EOG artifacts dominate important rhythmic components thus reducing the performance of BCI. Hence the EEG signal is required to be noise free. To suppress the artifacts, several methods have been used so far like independent component analysis (ICA), energy based threshold with multivariate empirical mode decomposition (MEMD). The ICA does not separate the sources completely and some of the meaningful EEG information is lost [3]. Another multivariate approach, BSS using well-known AMUSE [4] or a recent WASOBI [5] are also
MEMD decomposes EEG signals in to a dyadic filter bank structure where higher frequency range signals reside in the lower order intrinsic mode functions (IMFs) and lower frequency range signals are in higher order IMFs [7] [8]. Lower frequency EOG band are separated into the higher order IMFs. Due to energy variation between EEG and EOG, an energy based threshold is applied to remove the higher order IMFs containing EOG. But energy of some EEG is similar to EOG. Thus if threshold point is set too high, reconstructed EEG signal will contain artifacts with reduced signal quality. Again if the threshold point is set too low, much important rhythmic components will be rejected.
II. DATA AND METHODS A. Data Description To this end, experiments were conducted in the Advanced Brain Signal Processing Laboratory of RIKEN Brain Science Institute, Japan. Six subjects participated in affective empathy inducing experiments with visual facial stimuli. The EEG electrodes were connected to the 64 head channels as in extended 10/10 EEG recording systems and sampled with 2048Hz using BIOSEMI amplifiers. The electrode impedance was kept below 5kΩ. The experimental paradigm caused the subjects to move eyes frequently and unconsciously, causing ocular interference in EEG. For this reason, as a reference channel, EOG signals were recorded capturing eye movements and blinks.
B. Multivariate EMD Multivariate empirical mode decomposition (MEMD) produces the same number of IMFs for all channels, facilitating direct multichannel modeling and reducing mode mixing problem. MEMD produces the same number of IMFs for all channels, facilitating direct multichannel modeling and reducing mode mixing problem. Due to the filter bank property of MEMD, Fractional Gaussian Noise (fGn) channels which frequency spectrum is correlated to clean EEG, is added to the recorded EEG. Then MEMD is applied to this composite signal, resulting in proper separation of clean EEG and noise [10]. MEMD processes the input signal directly in a multidimensional domain. This data adaptive method produces empirical functions that represents hidden signal pattern. The MEMD algorithm [11] is given bellow: • Generate the pointset based on the Hammersley sequence for sampling on an (n-1) sphere. • Calculate a projection, denoted by ( )} , of the input along the direction vector , for all k signal { ( )} } as the (the whole set of direction vectors), giving set of projections. • Find the time instant { } , corresponding to the } . maxima of the set of projected signals ] for all values of k, to obtain • Interpolate [ , multivariate envelope curves ( )} . • For a set of K direction vectors, calculate the mean m(t) of the envelope curves as 1 ( ) (1) ( ) = • Extract the “detail” d(t) using d(t) = x(t) – m(t). If the “detail” d(t) fulfills the stoppage criterion for a multivariate IMF, apply the above procedure to x(t)-d(t), otherwise apply it to d(t) [9]. C. Noise Assisted MEMD NA-MEMD makes use of the quasi-dyadic filter bank properties of MEMD on white noise. An extra fGn channel is added to the original multivariate signal, and it is then processed via MEMD. The algorithm is as follows: • Create an uncorrelated Fractional Gaussian noise timeseries of the same length as that of the input. • Include the noise channel created in step 1 as a separate row with multivariate (N-channel) signal, obtaining an (N+1) channel signal. • Process the resulting (N+1) channel multivariate signal using the MEMD algorithm to obtain multivariate IMFs. • From the resulting (N+1) variate IMFs, separate two sets of IMFs – noise and the EEGs’ IMFs. Since required noise free EEG signal resides in similar dyadic frequency spectrum, the method can perfectly separate the frequency bands, hence is able to reduce the EOG artifacts.
D. Energy Based Thresholding NA-MEMD simultaneously decomposes all the multivariate signals into same number of IMFs in a perfect filter bank structure. The fGn channel is used as a reference signal to compare the energy to detect EOG related trends in EEG signal. The energy of the EOG signal is much higher than the EEG signals. The trend of EOG is determined by comparing the energy of individual IMF of the EEG channel with the same index IMF of the reference signal (fGn). Higher order IMFs contain the lower frequency components. The high frequency EEG signal of the channel can be easily separated by summing up the lower order IMFs as: ( )
= ∑
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( ) is the jth IMF of the nth channel. Here the where objective is to find the critical (threshold) IMF with index C(n) such that the IMFs of indices C(n), C(n)+1, … , j, are responsible for low frequency EOG artifacts. Then the EOG can easily be separated as: ( )
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where, ( ) is the final residue of the nth channel. It is assumed that the separated EOG signal includes some EEG components and it is required to be separated. The energy based thresholding algorithm is summarized below: 1. Decompose the analyzing multichannel EEG signal together with the fGn into a finite set of subbands called IMFs using NA-MEMD. 2. Calculate the energies of fGn’s IMFs and its 95% confidence interval (CI) 3. Compute the energies of all IMFs of raw EEG. Then find the lowest order IMF of EEG with energy exceeding the upper limit of CI. Thus obtained IMF index of the threshold IMF C(n). 4. The EEG of the nth channel is estimated using Eq. (2). E. Bandpass Filtering A filter shapes the frequency spectrum of the input signal, according to the magnitude of the transfer function. Butterworth filter has as flat a frequency response as possible in the passband. It is also referred to as a maximally flat magnitude filter. It exhibits a nearly flat passband with no ripple [12]. After performing energy threshold the extracted EOG artifact can contain some rhythmic components e.g. EEG. Then some important information will be lost from the reconstructed EEG signal. It is necessary to extract any remaining rhythmic component. A 4th order zero phase bandpass Butterworth filter has been used to extract any signal that falls in the frequency range of rhythmic components. Thus clean EEG signal with reduced information loss will be produced as:
(n) ( n) S EEG = SˆEEG + ρ (n)
(4)
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ρ (n ) is
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alpha, beta and gamma extracted from . Thus the pure EOG artifact of the nth channel is represented as:
(n) (n) S EOG = SˆEOG − ρ (n)
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Clean EEG signal is reconstructed summing all the lower order IMFs up to the threshold one. The rest of the IMFs are used reconstruct the EOG artifact. The raw EEG, the separated EEG and EOG using energy based thresholding (EBT) are illustrated in Fig. 3. 12
The experiments with real EEG signal including EOG artifact are described in the following section to evaluate the proposed method is EOG suppression.
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III. EXPERIMENTAL RESULTS A multivariate EEG signals obtained by the experiment described at the beginning of Section II are used to evaluate the proposed approach. The recorded EEG signals contain the eye blink artifact i.e. EOG. The EEG of channel 1 and its first 8 IMFs out 17 obtained by NA-MEMD are illustrated in Fig 1. It is noted that the higher order IMFs contains the lower frequency components of the EEG signal. Hence, the EOG artifact is mostly contained by the higher order IMFs. The energy based subband thresholding approach find the index of the IMF representing the threshold. Starting from the threshold IMF, all the higher order IMFs are responsible for the EOG artifacts. Thus the EOG is separated from the raw EEG signals.
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Fig. 2: Selection of threshold IMF index of EEG Ch-1. The 8th IMF is selected as the threshold point.
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The energy of each IMF for every channel is calculated including fGn using Eq. (2). Since fGn signal closely relates to clean EEG band, it is used as reference signal. The IMFs having energy lower than or closely similar to fGn energy are representative of rhythmic components and is reconstructed up to 95% confidence interval. The energy of fGn, its upper and lower limit of confidence interval and the energy of EEG signal (of ch-1) are shown in Fig. 2.
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Fig. 1: The EEG of Ch-1 and its 8 IMFs out of 17 obtained by NA-MEMD. Lower order IMFs represent higher frequency components.
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Fig. 4: Different rhythmic components extracted from EOG obtained by EBT.
It is observed that the extracted EOG signal still contains some high frequency components seems EEG signal. The clean EOG signal contains only low frequency trends [8]. Targeting
to separate the remaining EEG component, the extracted EOG signal is filtered with a zero phase 4th order Butterworth filter to extract any signal within the rhythmic component band. Then extracted rhythmic components from EOG are illustrated in Fig. 4.
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REFERENCES
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Fig. 5: Complete EOG separation; EOG separated by EBT (top), sum of extracted rhythmic components (middle) and EOG after extraction of rhythmic components (bottom).
All the rhythmic components are added to the extracted EEG signal obtained by EBT. The separated EOG artifact using EBT, the sum of extracted rhythmic component and the pure EOG are shown in Fig. 5. Finally the raw EEG, purified total EEG and completely separated EOG artifact are presented in Fig. 6. The experimental results confirm that the EOG separated by EBT method includes some EEG components and the proposed method effectively recovers such loss of EEG signal. IV. CONCLUSION A data adaptive novel method with noise assisted MEMD and further filtering approaches are implemented to effectively suppress the EOG artifact without any loss of EEG component. The fractional Gaussian noise (fGn) is used as the reference signal to implement the energy based thresholding (EBT) to separate the EOG from war EEG signal. Thus separated EOG still contains some rhythmic components of EEG which are required to be separated to recover the loss of EEG information. The zero phase bandpass filter is employed to extract these rhythmic components from EOG separated by EBT. The sum of the extracted components is added to the separated EEG to obtain the total purified EEG signal. Thus non-Cerebral artifacts are perfectly removed enhancing the quality of EEG signal for effective BCI implementation. The quantitative evaluation of the proposed method effective separation of EOG artifact is left for future work.
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