Improved ERP Classification Algorithm for Brain–Computer Interface of ALS Patient

The study on Amyotrophic Lateral Sclerosis (ALS) patient to identify the non-target or target stimulus based on event-related potential provide a way to improve P300 speller based Brain–Computer Interface (BCI). In the current work channel wise EEG data taken for the research. Feature extraction and Feature selection techniques based on Fourier and Wavelet transform and Statistics have been implemented to get the required features among the redundant one. By classifying the features categorized in 3 labels stated above by using support vector machine (SVM). The study reveals that the classification accuracy is improved in Morlet wavelet-based feature than statistical features for different channels taken in consideration.