Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface

<b>1. General description</b>Data was recorded using BCI2000 with g.USBamp (g.tec, Austria) EEG amplifier. 32 electrodes were used. Sampling rate was set to 600 Hz and data was bandpass filtered by the amplifier between 0.1 Hz and 60 Hz using a Chebyshev filter of order 8 and notch-filtered at 50 Hz. Data was stored as MATLAB mat-File, whereas each file is the data of one participant. <br><b>2. Experimental description</b>The experiment was split in three parts: spatial filter runs, training runs, testing runs. The stimulation patterns were presented with 60 bits per second and each run consits of 32 trials. During the testing runs, the runs were alternated between using random stimulation patterns and optimized stimulation patterns. As all trials were concatenated, this has to be taken into account. Furthermore, the participants had to perform the trials of each run in lexicographic order.<b><br></b><b>3. Variable description</b>Each file VP*.mat contains the following variables:- spfilter_data_x contains the raw EEG data of the spatial filter runs split by each m-sequence cyle. As there were 3 runs with 32 trials and 3 m-sequence cycles each, there are 3*32*3=192 m-sequence cycles in total. Since the m-sequence was shifted by 2 bits for each succesive target, the trials were lag-fixed previously. As the m-sequence consists of 63 bits, the data consists of 63/60*600=630 samples. Therefore the matrix has the following dimension: #m-sequences X #channels X #samples- spfilter_data_y contains the stimulation pattern for each m-sequenze cycle upsampled to be synchronized with the EEG data. The matrix has the following dimension: #m-sequences X #samples- train_data_x_*s contains the raw EEG data of the training runs split by trials. As different trial durations were used during training, * denotes the duration in seconds. The matrix has the following dimension: #trials X #channels X #samples- train_data_y_*s contains the stimulation pattern for each target during the training runs, upsampled to be synchronized with the EEG data. As the layout consits of 32 targets, the matrix has the following dimension: #trials X #targets X #samples- test_data_x contains the raw EEG data of the training runs split by trials. The trial duration was 2 seconds, therefore, there are 1200 samples per trial. The matrix has the following dimension: #trials X #channels X #samples- test_data_y contains the stimulation pattern for each target during the testing runs, upsampled to be synchronized with the EEG data. As the layout consits of 32 targets, the matrix has the following dimension: #trials X #targets X #samples There is an additional file "targetdelys.mat" containing a variable with the number of samples for each target (in lexicographic order) used to correct the raster latency.