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

this paper we consider the problem of linear representation or matrix factorization of a data set X, given in the form of a (m N)-matrix: X = AS, A , S , (1) where n is the number of source signals, m is the number of observations and N is the number of samples. Such representations can be considered as a new class of data mining techniques (or a concrete subclass of the above described data mining technique 3). In (1) the unknown matrices A (dictionary) and S (signals) may have some specific properties, for instance: 1) the rows of S are as statistically independent as possible --- this is the Independent Component Analysis (ICA) problem; 2) S contains as many zeros as possible --- this is the sparse representation problem or Sparse Component Analysis (SCA) problem; 3) the elements of X,A and S are nonnegative - this is nonnegative matrix factorization (NMF)