Brain-Computer Interface: Deep Learning Based classification of User Specific movement States from EEG data

Abstract

A method of developing interaction between brain and computer is provided by the Brain Computer Interface (BCI) device. These BCI devices use various invasive and non-invasive acquisition techniques to record the responses of brain. It is required to use the machine learning and pattern recognition techniques to translate these responses recorded from the brain for the control of any actions. This paper includes a feature extraction and classification techniques applied on data recorded from the brain. As well as we perform a classification based on machine learning technology methods to visualize the features of the arm movement using the 3-dimensional Class Activation Map (CAM). Secondly it reviewed; Autonomous Deep Learning (ADL) is a streaming online learning technique that uses Electroencephalography to distinguish five human fingers. Finally a kernel reinforcement learning based on clustering (RL) algorithm is reviewed that with less computational complexity, achieved a faster intelligence adaptation in brain regulation.

Then experimental results of above discussed methods are evaluated and compared using a BCI lab by considering a set of data samples.