Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework

TL;DR

This paper introduces a deep learning framework that emphasizes specific frequency domains in EEG signals to improve motor imagery classification, demonstrating promising results for BCI applications.

Contribution

The study presents a novel frequency emphasis method combined with CNNs for MI classification, validated on a standard dataset with promising accuracy improvements.

Findings

Average intra-session accuracy: 69.68%

Average inter-session accuracy: 52.76%

Frequency emphasis highlights MI-related brain activity

Abstract

The electroencephalogram, a type of non-invasive-based brain signal that has a user intention-related feature provides an efficient bidirectional pathway between user and computer. In this work, we proposed a deep learning framework based on corresponding frequency empahsize method to decode the motor imagery (MI) data from 2020 International BCI competition dataset. The MI dataset consists of 3-class, namely 'Cylindrical', 'Spherical', and 'Lumbrical'. We utilized power spectral density as an emphasize method and a convolutional neural network to classify the modified MI data. The results showed that MI-related frequency range was activated during MI task, and provide neurophysiological evidence to design the proposed method. When using the proposed method, the average classification performance in intra-session condition was 69.68% and the average classification performance in inter-session condition was 52.76%. Our results provided the possibility of developing a BCI-based device control system for practical applications.