Study of Feature Extraction Techniques for BCI Processing

Brain computer interface (BCI) is used to identify electrical activity in human brain using the electroencephalog-raphy (EEG). EEG records the electrical activity by placing the electrodes on the scalp. By using the recorded information it will able to classify the different types of abnormalities happening in brain. For extracting the information from signal without losing any information, some feature extraction methods have been used using deep learning concepts. The methods are Time Frequency distribution, Fast Fourier Transform, Eigen Vector, Wavelet Transform, Auto Regressive, Independent Component Analysis, Principal Component Analysis, Empirical Method Decomposition, Hilbert Huang Transform and Local Discriminant bases. Comparison have been done between the methods to show how effectively methods without losing an information. The classification accuracies of the feature methods are compared with each other. The study concluded the hybrid methods such as domain specific and automated features togeather shown better performance.