Ensemble learning-based eeg feature vector analysis for brain computer interface

Abstract

Brain Computer Interface (BCI) can be normally defined as the process of controlling the environment with the EEG signals. It is a real-type computer-based process which translates the human brain signals into necessary commands. Because there are many strokes attacked or neurologically affected patients in the world and they are not able to communicate effectively or share their emotions, thoughts with the outside world. Considering some extreme cases, tetraplegic, paraplegic (due to spinal cord injury) or post-stroke patients are factually ‘locked in’ their bodies, incompetent to exert strive motor nerves control after the stroke, paralyses or neurodegenerative diseases, requiring alternative techniques of interactive communication and control of organs. So, BCI is one of the best solutions for this purpose. This paper mainly discusses ensemble learning approaches for EEG signal classification and feature extraction. Bagging, Adaptive boosting, and Gradient boosting are quite popular ensemble learning methods, which are very effective for elucidation and explication of many practical classification problems. EEG signals may be classified as adopting a set of features like autoregression, power spectrum density, energy entropy, and linear complexity. In this paper, we have used three illustrative procedures Adaptive Boosting, Bagging, and Gradient Boosting ensemble learning methods and extracted features using discrete wavelet transform (DWT), autoregression (AR), power spectrum density (PSD), and common spatial pattern (CSP).