Abstract:
Robotics and Artificial Intelligence have played a significant role in the development
of assistive technologies for people with motor disabilities. Brain-Computer Interface
(BCI) is a type of communication system that allows humans to communicate with
their environment by detecting and quantifying control signals produced from different
modalities, and translating them into voluntary commands for actuating an external
device. This project deals with the classification of two-class and multi-class motor
imagery electroencephalography (EEG) signals and translating them into voluntary
commands for actuating an external device through a brain-computer interface (BCI).
For efficient classification of EEG signals, a new framework has been proposed, that
employs a combination of Butterworth bandpass filter and ICA for pre-processing, and
CSP & log-variance for feature extraction, along with different classification
techniques such as SVM, LDA, naïve Bayes, decision trees, k-NN, and logistic
regression to choose the most relevant classifier to obtain a significant improvement in
the average classification accuracies of various datasets as compared to the approaches
proposed earlier. Classification is performed using the MATLAB platform. The
classified signals are then fed to the embedded system to operate an actuator
