A novel feature extraction scheme for N400 detection

The presented work proposes a simple feature extraction technique which is designed for robust detection of event related potentials (ERP). This technique was tested to detect the N400 which is an ERP generally associated with recall. The chief advantages of the proposed technique are that it is robust to different ocular artifacts and yet sensitive to event related potentials. Further each signal will correspond to only a few features as opposed to 100s and 1000s of features obtained by traditional feature extraction techniques. The proposed steps involve a) Computing the first and second order difference of the data b) measuring mean and variance respectively for first and second order differencing over 1 second windows c) repeating the steps a and b after lagging the signal by 0.5 seconds. Differencing computes the change in amplitude of EEG signals, which is considered important in ERP analysis. Step (b) is a unique way of getting rid of abrupt signal changes which are artifacts, as for abrupt changes in the signal; the computed variance of the second difference is high. Also, computing windowed average of the first difference reveals the (increasing/ decreasing) trend of the data. Step (c) ensures that potential changes are not missed if they lie across two windows during the first phase of windowing. The proposed approach of feature extraction by the above steps outperforms three established traditional feature extraction schemes in identifying N400 waveforms using support vector machines. The average classification accuracy obtained by the proposed feature set is 96.91%.