Brain-Computer Interface (BCI) is a system that measures and processes the activity of the human brain to improve or replace the function of the human body. In the future, this system can be a solution for people with disabilities, especially in locomotor organs such as hands and feet. The purpose of this research is to classify four classes of electroencephalogram (EEG) signals that represent four human motor imagery. The four motor imageries are left-hand, right-hand, left-foot, and right-foot that originated from motor imagery dataset. The proposed method in this research consists of filtering, feature extraction, and classification. The proposed method employed the Finite Impulse Response (FIR) in the filtering process to pass the required EEG signals such as delta, theta, alpha, beta, and gamma channels. The features are the Root Mean Square (RMS) values from the time domain filtered signal. Our system design used these features as input classification method that used the Artificial Neural Network (ANN). The training and testing data separation used 10-fold cross-validation. To analyze the testing performance used a confusion matrix. Based on the results, the proposed method brings the highest system accuracy as 61.2% on the beta channel.
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