Publication

Abstract : In the present days, Brain Computer Interfaces (BCI) are used in applications pertaining to diagnostics and prosthetics for neurological disorders, navigation of unmanned aerial vehicles and gaming. Detailed analysis of spectral features and classifiers using eye blink control from Electroencephalogram (EEG) will be described in this paper. In this study, the signals were acquired using an EEG headset, where the ocular pulses dominated the data. Principal Component Analysis was used to extract the ocular components. From the resultant signal, the features: sum of spectral peaks, bandwidth, power spectral entropy, and Cepstral coefficients of the blinks were extracted for supervised learning. The classification methods Multiclass Support Vector Machine (SVM), Quadratic Discriminant Analysis (QDA) and Artificial Neural Networks (ANN) were evaluated using these features independently as well as together. The results showed that among the three features, spectral peaks and bandwidth gave more classification accuracy. Also while features were taken together, QDA gave superior classification results in terms of accuracy, sensitivity and specificity compared to Multi class SVM and ANN.