Classification Of EEG Imagery Motor Function Using 3D Convolutional Neural Network - Kanesan, Thivagar (Universiti Teknologi PETRONAS, 2020)
A brain-computer interface (BCI) is a computer-based system that acquires brain signals, analyzes them, and translates them into commands. One of the main uses for BCI is motor imagery, which has countless potential ranging from control over prosthetic limbs to cybertronics. This project consists of research done towards braincomputer interfacing mainly using the outputs generated from EEG signals of left/right imagined arm movements. This EEG output signal will then be classified using deep learning technique known as 3D convolutional neural network to create a classification algorithm. 3D ConvNet is well-suited for spatiotemporal feature learning, where convolution and pooling operations are performed spatio-temporally. Compared to 2D ConvNet, 3D ConvNet has the ability to model temporal information better owing to 3D convolution and 3D pooling operations.