World's Fastest Brain-Computer Interface: Combining EEG2Code with Deep Learning

<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.<br><br><b>2. Experimental description</b>The experiment was split in a training phase and a testing phase. During both, the participant had to focus a target which was modulated with fully random stimulation patterns, which were presented with 60 bits per second.For training, the participant had to perform 96 runs for, each with 4 s of stimulation, which means a total of 96*4*60=23040 bits were presented. For testing, the participant also had to perform 96 runs, but with 5 s of stimulation, which results in 96*5*60 = 28800 Bits.<b><br></b><b><br></b><b>3. Variable description</b>The file <b>VP1.mat</b> contains the following variables:- train_data_xcontains the raw EEG data of the training runs split by runs. The matrix has the following dimension: #runs X #channels X #samples- train_data_ycontains the stimulation pattern for each train run, upsampled to be synchronized with the EEG data. The matrix has the following dimension: #runs X #samples- test_data_xcontains the raw EEG data of the test runs split by runs. The matrix has the following dimension: #runs X #channels X #samples- test_data_ycontains the stimulation pattern for each test run, upsampled to be synchronized with the EEG data. The matrix has the following dimension: #runs X #samples<br>The file <b>VP1.hdf5 </b>is the Keras CNN model which was trained during the online experiment.<br>The file <b>EEG2Code.py</b> is a python script which takes the MAT-file as input and outputs the pattern prediction accuracy for each of the test run. It must be noted that the script searches for a Keras model with the file name as the MAT-file (but with hdf5 file extension). If the model exists, it will be loaded, otherwise a new model will be trained.