EEG Signal Classification of Motor Imagery Right and Left Hand using Common Spatial Pattern and Multilayer Perceptron Back Propagation

Abstract

The number of people with disabilities is increasing, so it requires bionic devices to replace human motor functions. Brain-Computer Interface (BCI) can be a tool for the bionic device to communicate with the brain. Signal brain or Electroencephalogram (EEG) signal need to classify to drive the corresponding bionic device. This research goal is to classify the imagination of the right and left-hand movements based on the EEG signal. The system design in this research consists of EEG channel selection using Finite Impulse Response (FIR) filter, feature extraction using Common Spatial Pattern (CSP), and classification using Multilayer Perceptron Back Propagation (MLP-BP). The data used a secondary dataset from BCI Competition IV (2b) with 9 research subjects. The research scenario is carried out by trying to use several variations in the number of hidden layer nodes on each EEG channel. Based on the test, the best accuracy for MLP-BP is 68.7% using 24 nodes in the alpha channel.