Description

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imagery movement. Brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, Motor Imagery (MI)-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. We proposes a machine learning algorithms to efficiently analysis the motor imagery based brain computer interface by using electroencephalograph. The Extra Tree Classifier and Random Forest classifier are implemented and analysis the EEG data and detects the right hand, left hand, both feet and tongue and generates the result in terms of metrics like, accuracy, precision, recall and f1-score.