Department of Mechatronics Engineering, National University of Sciences & Technology, Islamabad, Pakistan.
Robot Design and Development Lab, National Centre of Robotics and Automation (NCRA), Punjab, Pakistan.
PLoS One. 2023 Sep 8;18(9):e0276133. doi: 10.1371/journal.pone.0276133. eCollection 2023.
Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.
机器人技术和人工智能在为运动障碍患者开发辅助技术方面发挥了重要作用。脑机接口 (BCI) 是一种通信系统，通过检测和量化来自不同模式的控制信号，并将其转换为用于驱动外部设备的自愿命令，允许人类与其环境进行通信。为此，分类大脑信号具有非常高的准确性和最小化错误对于研究人员来说非常重要。因此，在这项研究中，提出了一种新的框架来分类二进制脑电 (EEG) 数据。所提出的框架在 BCI 竞赛 IV 数据集 1 和 BCI 竞赛 III 数据集 4a 上进行了测试。通过预处理去除 EEG 数据中的伪影，然后进行特征提取，以识别记录脑信号中的鉴别信息。信号预处理包括对原始 EEG 数据应用独立成分分析 (ICA)，并结合共空间模式 (CSP) 和对数方差来提取有用的特征。比较了六种不同的分类算法，即支持向量机、线性判别分析、k-最近邻、朴素贝叶斯、决策树和逻辑回归，以准确分类 EEG 数据。对于两个数据集，所提出的框架都使用逻辑回归分类器获得了最佳的分类准确率。对于七个不同的受试者，在 BCI 竞赛 IV 数据集 1 上获得了 90.42%的平均分类准确率，而在 BCI 竞赛 III 数据集 4a 上，对于五个受试者，获得了 95.42%的平均准确率。这表明该模型可以用于实时 BCI 系统，并为 2 类运动想象 (MI) 信号分类应用提供卓越的结果，并且通过一些修改，该框架也可以在未来兼容多类分类。
