Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology, Madras, 600036, India.
Biomed Phys Eng Express. 2024 Apr 8;10(3). doi: 10.1088/2057-1976/ad3647.
A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.
基于运动想象 (MI) 的脑机接口 (BCI) 系统旨在为运动障碍者和脑损伤患者（例如中风患者）等提供神经康复。本工作的目的是通过利用 MI 任务期间脑电图 (EEG) 中事件相关去同步和同步 (ERD/ERS) 的发生来对左手和右手 MI 任务进行分类。本研究提出使用一组具有频率相关宽度的复 Morlet 小波 (CMW) 来生成通道 C3 和 C4 中 MI EEG 信号的高分辨率时频表示 (TFR)。在此研究了一种用于选择与 CMW 中心频率相关的循环数的值的新方法，用于提取 MI 任务特征。生成的 TFR 作为输入提供给卷积神经网络 (CNN)，以将其分类为左手或右手 MI 任务。所提出的框架在 BCI 竞赛 IV 数据集 2a 上达到了 82.2%的分类准确率，表明与基线方法和其他现有算法相比，本工作生成的 TFR 具有更高的分类准确率。
