Abstract: We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive ‘pop ’ functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A ‘plug-in ’ facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available

A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. If accurate solutions to such problems could be obtained, neurologists would gain non-invasive access to patient-specific cortical activity. Access to such data would ultimately increase the number of patients who could be effectively treated for neural pathologies such as multi-focal epilepsy. However, estimating the location and distribution of electric current source within the brain from electroencephalographic (EEG) recordings is an ill-posed problem. Specifically, there is no unique solution and solutions do not depend continuously on the data. The ill-posedness of the problem makes finding the correct solution a challenging analytic and computational problem. In this paper we consider a spatio-temporal method for sources localization, taking advantage of the entire EEG time series to reduce the configuration space we must evaluate. The EEG data is first decomposed into signal and noise subspaces using a Principal Component Analysis (PCA) decomposition. This partitioning allows us to easily discard the noise subspace, which has two primary benefits: the remaining signal is less noisy, and it has lower dimensionality. After PCA, we apply Independent Component Analysis (ICA) on the signal subspace. The ICA algorithm separates multichannel data into activation maps due to temporally independent stationary sources. For each activation map we perform an EEG source localization procedure, looking only for a single dipole per map. By localizing multiple dipoles independently, we substantially reduce our search complexity and increase the likelihood of efficiently converging on the correct solution.

We report a study on two-person game playing involving simultaneous EEG recording from both subjects. Independent Component Analysis is used for identifying activities of individual cortical EEG sources. Activity of a midline fronto-central component is identified in four of five subjects. This component accounts for the P300 waveform whose amplitude varies depending on the success in the gaming situation. 1

This work is about the development of an alternative way of averaging evoked potentials (EP) of cognitive activities. Since the main assumption of invariant waveforms time locked to the eliciting events does not hold for cognitive EPs, averaging results in distorted estimates. Our alternative selective averaging finds similar subsequences of fixed length with variable latency which are common to all multi-channel EPs by transforming the multivariate time series to discrete sequences via vector quantization and applying a sequence alignment algorithm. The method yields a significant improvement over common averaging in terms of noise attenuation and is shown to be valid by comparison with results for random data. Results for EP data obtained during a spatial imagination task are reported. 1 Introduction An evoked potential (EP) is the electro cortical potential measurable in the human electro encephalogram (EEG) before, during and after sensoric, motoric or cognitive events. This work...

One of the standard applications of Independent Component Analysis (ICA) to EEG is removal of artifacts due to movements of the eye bulbs. Short blinks as well as slower saccadic movements are removed by subtracting respective independent components (ICs). EEG recorded from blind subjects poses special problems since it shows a higher quantity of eye movements which are also more irregular and very different across subjects. It is demonstrated that ICA can still be of use by comparing results from four blind subjects with results from one subject without eye bulbs who therefore does not show eye movement artifacts at all.

Purpose The purpose of this study was to further localize cerebral perfusion abnormalities, and to better correlate these abnormalities with the clinical severity of Parkinson’s disease (PD). Methods A single-photon emission computed tomography (SPECT) study was performed on 27 patients with PD and 24 age-matched controls. SPECT images were spatially normalized, concatenated, and then decomposed using Infomax independent component analysis (ICA). The resulting image components were separated by logistic regression into two subspaces: “disease-related ” components whose subject weights differed between groups, and

Abstract Sensor selection is typically used in magnetoencephalography (MEG) and scalp electroencephalography (EEG) studies, but this practice cannot differentiate between changes in the distribution of neural sources versus changes in the magnitude of neural sources. This problem is further complicated by (1) subject averaging despite sizable individual anatomical differences and (2) experimental designs that produce overlapping waveforms due to short latencies between stimuli. Using data from the entire spatial array of sensors, we present simple multivariate measures that (1) normalize against individual differences by comparison with each individual’s standard response; (2) compare the similarity of spatial patterns in different conditions (angle test) to ascertain whether the distribution of neural sources is different; and (3) compare the response magnitude between conditions which are sufficiently similar (projection test). These claims are supported by applying the reported techniques to a short-term word priming paradigm as measured with MEG, revealing more reliable results as compared to traditional sensor selection methodology. Although precise cortical localization remains intractable, these techniques are easy to calculate, relatively assumption free, and yield the important psychological measures of similarity and response magnitude.