Shiam Abdullah Al, Hassan Kazi Mahmudul, Islam Md Rabiul, Almassri Ahmed M M, Wagatsuma Hiroaki, Molla Md Khademul Islam
Department of Computer Science and Engineering, Sheikh Hasina University, Netrokona 2400, Bangladesh.
Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh 2224, Bangladesh.
Brain Sci. 2024 May 3;14(5):462. doi: 10.3390/brainsci14050462.
Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.
脑电图（EEG）被有效地用于描述与脑机接口（BCI）实现中运动功能的不同任务相对应的认知模式。明确的信息处理对于降低实际BCI系统的计算复杂度是必要的。本文提出了一种基于熵的方法，用于在脑机接口（BCI）系统中选择有效的EEG通道进行运动想象（MI）分类。该方法识别出具有较高熵得分的通道，这表明信息含量更大。它丢弃冗余或有噪声的通道，从而降低计算复杂度并提高分类准确率。高熵意味着更无序的模式，而低熵意味着信息较少的较有序模式。计算每个试验中每个通道的熵。每个通道的权重由该通道在所有试验中的平均熵表示。选择一组具有较高平均熵的通道作为MI分类的有效通道。通过分解所选通道创建有限数量的子带信号。为了提取空间特征，将共同空间模式（CSP）应用于EEG信号的每个子带空间。基于CSP的特征用于使用支持向量机（SVM）对右手和右脚MI任务进行分类。使用两个公开可用的EEG数据集（即BCI竞赛III-IV(A)和BCI竞赛IV-I）验证了所提出方法的有效性。实验结果表明，所提出的方法优于前沿技术。
