EEG Signal Processing For Motor Imagery Direction of Hand Movement Using the Brain Computer Interface

摘要

In Indonesia, there are at least 10 million individuals with various forms of disabilities, accounting for 4.3% of the population according to the latest census. With technological advancements, numerous robots have been developed to enhance human life by increasing convenience and efficiency. Brain-computer interfaces (BCIs) are emerging as an alternative for controlling robots by interpreting human brain signals. Motor Imagery-BCI (MI-BCI) systems primarily utilize electroencephalogram (EEG) technology to measure brain activity. Subsequently, classification is performed using the Convolutional Neural Network (CNN) method to determine the movements of the robot arm. The results of this system are analyzed to draw conclusions from the research. The accuracy of the CNN classification results can be calculated through 30 experiments, resulting in an average accuracy rate of 85.64%.