EEG-Based Frequency Domain Separation of Upward and Downward Movements of the Upper Limb

For a seamless integration of electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCIs), it is vital to be able to classify movements of the same joint. However, a fundamental challenge in classifying the same joint movements arises from the close spatial proximity of the corresponding brain regions. To address this challenge, we explore the feasibility of distinguishing up and down movements specific to the right upper limb using multiple frequency bands combined with a channel averaging method. Six electrodes positioned in close proximity to the motor cortex and two distinct frequency bands: mu (8-12Hz) and beta (12-30Hz) were selected. This isolates and enhances electromagnetic activity in the brain commonly associated with motor and cognitive processing. The results of our study revealed promising outcomes across two classification methods. Utilizing a support vector machine (SVM) classifier, our proposed approach achieved an average accuracy of 59.3% and a k-nearest neighbor(KNN) classifier approach yielded an average accuracy of 61.63% in distinguishing between upward and downward movements of the right arm. These results demonstrate the potential of combining spatially focused EEG acquisition with frequency-specific analysis for improved MI-BCI performance.