SigMate: A Comprehensive Software Package for Extracellular Neuronal Signal Processing and Analysis Mufti Mahmud, Graduate Student Member, IEEE, Alessandra Bertoldo, Stefano Girardi, Marta Maschietto, Elisabetta Pasqualotto, and Stefano Vassanelli Abstract—This paper presents a comprehensive neuronal signal analysis and processing tool, ‘SigMate’, developed in MATLAB, incorporating the available standard tools and our inhouse custom tools. The present features include, data visualization (2D and 3D), slow stimulus artifact removal (including baseline correction), noise characterization, file operations (file splitting, concatenation, and column rearranging), latency estimation in local field potentials (LFPs), current source density (CSD) analysis, determination of cortical layer activation order (CLAO) from LFPs and CSDs, spike detection, spike sorting, single sweep LFP sorting, EEG based brain-machine interfacing (BMI), neuronal simulation environment, and are gradually growing. This tool has been extensively tested using signals recorded by the standard electrodes as well as implantable EOSFETs (Electrolyte-OxideSemiconductor Field Effect Transistors) based multisite neural probes and will be made available to the community shortly. Keywords—Neuronal signal analysis; Neuronal signal processing; local field potentials; neuronal activity; signal analysis software.
I. INTRODUCTION
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EURONAL signals recorded by means of noninvasive/invasive neuronal probes require rigorous processing and analysis to understand the underlying neuronal network activity caused by stimuli. Advances in the microelectronics and microelectrode technology have enabled the scientists to record from hundreds of neurons and also simultaneously from a number of channels [1], [2]. Inferring meaningful conclusions by analyzing this massive amount of data recorded from noisy experimental conditions is a big challenge for the neuroscience and neuroengineering community [1]. Though individual tools are available to
perform processing of the spike train analysis, spike detection and sorting, yet very few software tools are available to date which integrate all the signal processing steps [3], [4]. There are a few software packages developed for academic and commercial purposes [3], [4], [5]. These software packages mainly deal with data visualization, spike detection and sorting, spike train analysis, processing and analysis of signals recorded using multi–electrode arrays, EEG signal analysis, and cross–software platform. Also, a platform is under development to promote the sharing of laboratory-developed software across the worldwide web [6]. However, there is no comprehensive standard tool/package to date incorporating analysis on spike trains, LFPs, EEG based interfacing, and basic neuronal simulations. Moreover, data format conversion and using multiple packages on a single dataset to perform different operations are time consuming and cumbersome. Thus an umbrella tool is required to perform these file operations. In this paper we present our in–house algorithms to process and analyze LFPs, a novel way to couple EEG and robotic device, basic neuronal simulations based on Hodgkin–Huxley models, along with a standard spike train analysis tool bundled together in a software package called, ‘SigMate’. Developed in M ATLAB (version 7.9, R2009b, http://www.mathworks.com/) and tested in Windows 32 and 64–bit versions, this is a multipurpose package (dealing with spikes, LFPs, EEGs, and simulations) for processing and analyzing neuronal signals recorded using neuronal probes. The friendly Graphical User Interface (GUI) environments facilitate the usage of non–programming background users. II. METHOD
Manuscript received December 14, 2010. This work was supported by the European Commission under the Seventh Framework Programme (ICT2007.8.3 Bio-ICT convergence, 216528, CyberRat). Mufti Mahmud is with the NeuroChip Laboratory of Department of Human Anatomy & Physiology and Department of Information Engineering, University of Padova, 35131, Padova, Italy (e-mail:
[email protected]). Alessandra Bertoldo is with the Department of Information Engineering, University of Padova, 35131, Padova, Italy (e-mail:
[email protected]). Stefano Girardi (e-mail:
[email protected]), Marta Maschietto (e-mail:
[email protected]) and Elisabetta Pasqualotto (e-mail:
[email protected]) are with the NeuroChip Laboratory of Department of Human Anatomy & Physiology, University of Padova, 35131, Padova, Italy. Stefano Vassanelli is with the NeuroChip Laboratory of Department of Human Anatomy & Physiology, University of Padova, 35131, Padova, Italy (Corresponding Author ; phone: +39 049 8275337; fax: +39 049 8275331; e-mail: stefano.vassanelli@ unipd.it).
The software package is designed to perform various processing and analysis on the neuronal signal files. The main functionalities included at present are: data display (2D and 3D) with zooming, panning and data cursor options, slow stimulus artifact removal (including baseline correction), noise characterization with noise estimation and baseline correction, basic file operations (including file splitting, file concatenating and file column rearranging), latency estimation (in LFPs and CSDs) with the possibility to detect the CLAO, EEG based robotic device interface, Hodgkin–Huxley based neuronal simulation environment, and the spike train analysis package adopted from Quiroga et al., 2004 [3]. Apart from the spike train analysis module, the
rest of the features are our in–house developed algorithms which are tested rigorously with datasets recorded using standard borosilicate micropipette and EOSFETs from anesthetized rats. The following subsections describe the individual modules in greater details. Figure 1 shows the multilayered architecture of the software package and figure 2 shows the use case diagram.
and root mean square. These basic operations are useful to preliminarily understand the quality of the signals. B. File Operations Few basic file operations are being incorporated in the software package that are often time consuming for the scientists who use different software for signal recording and performing signal processing and analysis. These operations include: file splitting (splits multi-sweep file into many single-sweep files based on sampling frequency), file concatenation (concatenates multiple single-sweep files into a multi-sweep file), and file column rearranging (retains only selected channels and eliminates the unselected ones). C. Artifact Removal
Fig. 1. Multilayered architecture of the SigMate software package.
This module performs artifact removal as well as baseline correction. It expects the control signals (recorded without providing stimulation) and signals with evoked response upon stimulation. It utilizes an in–house algorithm for detection of peak–valley pairs in a signal. For each peak there is a corresponding valley which constitutes a peak– valley pair. The average of this peak–valley pair gives the estimated point of the signal part in which the pair is detected. Therefore, the averages of these pairs provide an estimation of the signal [7]. The mean of this estimation is subtracted from a signal for the baseline correction. The estimation of the control signal is subtracted from the evoked signal to remove artifact from the evoked signal. D. Noise Characterization The noise characterization module assesses the quality of the recorded signals and quantifies the noise present in the signals. It uses in-house algorithms for the detection of the steady-states of a sweep. These steady-states are detected as the prestimulus part (first steady-state, FSS) and the part of the signal from the end of the evoked response until the end of the signal (second steady-state, SSS) through comparison of the standard deviation of the signal. Once the steady-states are detected, mathematical models are fitted to calculate the measurement error (ME) present in the signal [8]. First order statistical information such as mean and standard deviation of the ME are used to quantify the noise. Also, distribution of the noise and its estimation is shown. E. Latency Estimation and CLAO Determination
Fig. 2. Use case model of the SigMate software package.
A. Data Display This is the initial GUI of the package containing links to other modules of the package. It has a number of functionalities along with the signal visualization (in 2D and 3D) which include averaging of single sweeps, estimating the noise, perform +/- averaging, and calculate the mean square
This module calculates latencies in the LFPs, CSDs and saves these latencies for determining the CLAO. 1) Event Detection and Latency Computation in LFPs To detect latencies in the LFPs, it calls a function capable of detecting the events present in an LFP signal [9]. After detecting the events, latencies are calculated as the differences between detected events and stimulus-onset. 2) Latency Computation in CSDs From the LFPs, CSDs are calculated using four different methods: standard CSD analysis method, delta–inverse CSD
method, step–inverse CSD method, and spline–inverse CSD method [10]. However, we used the delta–inverse CSD method (δ-iCSD) for calculating the CSD profile to estimate the latencies as the difference between the first sink’s peak and the stimulus onset [11]. 3) Determination of Cortical Layer Activation Order Once the latencies in the LFPs and CSDs are calculated, the CLAO is automatically determined using them. The latencies from both the LFPs and CSDs are layerwise grouped basing on the a priori information about the recording depths. For the LFPs, the CLAO is defined as an ascending ordered list of minimum latencies of the second event in each layer. Similarly, the CLAO from the CSDs are calculated by sorting the minimum layerwise latencies in ascending order [11].
III. RESULTS AND DISCUSSION The individual modules (except EEG based BMI and neuronal simulation) are extensively tested with signals recorded using standard micropipette and EOSFETs based planar and implantable chips from anesthetized rat brains. Figure 3 shows 3D plotting as an example of cortical surface propagation recorded using planar EOSFETs [14].
F. Single Sweep LFP Clustering Clustering or classification of single sweep LFPs plays an important role in understanding the underlying neuronal network that generated the signals. As shape information is very important in LFPs, this module exploits the contour information for performing the classification. The main steps are: template generation, single sweep recognition through template matching, and clustering single sweeps based on intelligent K-Means clustering [12]. Once the signals are clustered, local averages of the clusters are computed, events in each local average are detected, and latencies of the events are calculated. The amplitudes and the latencies of the events are stored for further processing.
Fig. 3. Example of 3D plotting, showing propagation of cortical surface signals over 13 EOSFETs.
The artifact removal module performs baseline correction and removes slow stimulus artifacts from the recordings effectively [8].
G. Spike Detection, Sorting and Train Analysis The SigMate provides an interface with the spike detection and sorting software as described in [3] which uses an automated threshold based on the background noise of the signal for spike detection, wavelet transformation for spike sorting and superparamagnetic clustering technique for clustering the spikes. H. EEG Based BMI This module provides an interface to a less complicated BMI model capable of acquiring the EEG evoked by saccadic movement to drive a robotic device through a predefined path [13]. This interface is capable of acquiring EEG and conditioning the acquired EEG signals to generate the binary control signals used in driving the robotic device. I. Neuronal Simulation Environment SigMate also contains a neuronal simulation environment with simple Hodgkin-Huxley based single neuron models capable of performing stimulus optimization, another modified Hodgkin–Huxley model (Ca2+ based) along with a waveform generator capable of delivering stimuli in various shapes (sine, sawtooth, reverse sawtooth, and pulse) with varying frequency and amplitudes. This module helps in finding out single neuron response upon applying particular stimuli in current–clamp or voltage–clamp mode.
Fig. 4. Slow stimulus artifact removal by the artifact removal module.
The noise characterization module provides with successful noise characterization of various datasets with a limitation in case of signals with very low signal-to-noise ratio and high oscillations [9]. It also shows quantitatively the quality of the recordings through the mean and standard deviation of the calculated measurement error.
Fig. 5. Example of noise characterization of the SSS in a signal file recorded using planar EOSFETs.
The latency estimation module is capable of calculating latencies in LFPs and CSDs. For the LFPs recorded from the rat barrel cortex, the events are determined [11]. The barrel cortex LFPs evoked by whisker stimulation are represented by four subsequent events. Our in-house event detection algorithm detects these events present in the LFPs and calculates latencies. CSDs are calculated using δ-iCSD, and latencies are calculated. Figure 6 shows the CSD profile
corresponding to an LFP profile. From these latencies the CLAO are automatically calculated (seen in figure 7).
Fig. 6. (A) The LFP profile recorded from rat barrel cortex. (B) CSD profile calculated using δ-iCSD from the LFP profile. The alphabets and numbers denote the sinks and sources.
IV. CONCLUSION As with the growth of neuronal probes, amount of acquired data are increasing, the need of one single software package performing all necessary processing and analysis on the data has become crucial. This is the first step towards meeting that need. As the software has been extensively tested with two possible sources of data, we believe that once it is disseminated to the community (which will happen in the near future) it will serve a good deal in analyzing neurophysiological signals. We are working on neuronal network based on different stimuli to be able to predict the signals a priori, compare them with the recorded signals and understand the activation of underlying neuronal networks generating the signals. REFERENCES [1] [2]
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[4] Fig. 7. Comparison of CLAO from LFPs and CSDs.
The single sweep clustering method applied on a number of datasets with successful clustering of single sweeps [12]. Figure 8 shows a representative result of such a clustering.
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Fig. 8. Clustering of 100 single sweep LFPs with their local averages.
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