Contributor: Ray Smith BE MIEI Dept. of Electronic and Electrical Engineering University College Dublin, Belfield, Dublin 4 Rep. of Ireland Supervisior: Dr. Richard Reilly DSP Research Group, UCD Description of Methodology Preprocessing: Utilizing the 1000Hz trials each epoch was LPF filtered (fc=45Hz) and then downsampled to 100Hz. Each epoch was then zero-meaned. Channel Selection: Cross correlative analysis between L & R trials proved that electrode positions located above the primary motoral cortex maximally distinguished between trials, thus supporting physiological beliefs. Depending on the feature extraction technique employed, an optimum subset of the optimum electrodes positions found (FC1 FC2 C1 C2 C3 C4 CP1 CP2 etc) were chosen based on classification accuracy of the training data. (10 shuffle by 10 fold cross validation). Overall aim here is to reduce Feature Vector dimensionality in the hope of developing a simple classifier capable of real time implementation Feature Selection: A collection of time and frequency domain features associated with motoral movement were investigated. The overall scheme was a weighted contribution of the various techniques ARX An Autoregressive with Exogenous input (ARX) model for combined filtering and feature extraction was employed as a natural extension to an Autoregressive (AR) parametric modelling technique. The exogenous input is an ensemble average of all trials for a given channel specific to an upcoming left or right movement. The ensemble average is represenative of the underlying Bereitschaftspotential (BP), a gradually rising negative potential focused above the motoral cortex occurring about 1000ms preceeding the onset of movement, as the random background EEG activity averages out about the mean. The ARX case of modelling both the signal, the underlying BP, and the noise, background EEG assuming no artifacts, is more physiologically reasonable and yields better results than AR modelling alone. (85% compared to 60%) Power Band Analysis (ERD/ ERS) The mean power in two bands, extensions of the mu and beta bands (8-16Hz and 17-25Hz) for selected channels above the primary motoral cortex. Each epoch was windowed (hanning) and then the fft was taken to estimate the PSD. Time - Frequency Analysis Estimation of the power in the extensions of the mu and beta bands (8-16Hz and 17-25Hz) over overlapping windows 40% the length of the epochs using a STFT. Classifier A simple linear discriminant function (approx. to Bayes rule) was employed to classify the feature vectors. The computational simplicity of this approach is a great advantage. Training Data Accuracy using 10 shuffle by 10 fold cross validation was 84%.