Decoding Brain Motor Imagery with various Machine Learning techniques
This is an incremental study applying existing methods to new data in the domain of BCI for motor imagery classification.
The paper tackled decoding brain motor imagery signals for brain-computer interfaces by applying various machine learning methods to predict subjects' intent from modulated brain signals, but no concrete results or numbers were reported.
Motor imagery (MI) is a well-documented technique used by subjects in BCI (Brain Computer Interface) experiments to modulate brain activity within the motor cortex and surrounding areas of the brain. In our term project, we conducted an experiment in which the subjects were instructed to perform motor imagery that would be divided into two classes (Right and Left). Experiments were conducted with two different types of electrodes (Gel and POLiTag) and data for individual subjects was collected. In this paper, we will apply different machine learning (ML) methods to create a decoder based on offline training data that uses evidence accumulation to predict a subject's intent from their modulated brain signals in real-time.