Biomedical Engineering Department, University of Montreal, Montreal, Canada.
Comput Intell Neurosci. 2019 Oct 9;2019:2503431. doi: 10.1155/2019/2503431. eCollection 2019.
A brain-computer interface (BCI) decodes the brain signals representing a desire to do something and transforms those signals into a control command. However, only a limited number of mental tasks have been previously investigated and classified. This study aimed to investigate two motor imagery (MI) commands, moving forward and moving backward, using a small number of EEG channels, to be used in a neurofeedback context. This study also aimed to simulate a BCI and investigate the offline classification between MI movements in forward and backward directions, using different features and classification methods. Ten healthy people participated in a two-session (48 min each) experiment. This experiment investigated neurofeedback of navigation in a virtual tunnel. Each session consisted of 320 trials where subjects were asked to imagine themselves moving in the tunnel in a forward or backward motion after a randomly presented (forward versus backward) command on the screen. Three electrodes were mounted bilaterally over the motor cortex. Trials were conducted with feedback. Data from session 1 were analyzed offline to train classifiers and to calculate thresholds for both tasks. These thresholds were used to form control signals that were later used online in session 2 in neurofeedback training to trigger the virtual tunnel to move in the direction requested by the user's brain signals. After 96 min of training, the online band-power neurofeedback training achieved an average classification of 76%, while the offline BCI simulation using power spectral density asymmetrical ratio and AR-modeled band power as features, and using LDA and SVM as classifiers, achieved an average classification of 80%.
脑机接口 (BCI) 解码代表执行某种动作的愿望的脑信号，并将这些信号转换为控制命令。然而，以前仅研究和分类了有限数量的心理任务。本研究旨在使用少量 EEG 通道研究两种运动想象 (MI) 命令，向前移动和向后移动，用于神经反馈环境。本研究还旨在模拟 BCI，并使用不同的特征和分类方法离线分类向前和向后运动的 MI 运动。十位健康人参加了一项两阶段（每次 48 分钟）实验。该实验研究了在虚拟隧道中导航的神经反馈。每个阶段包括 320 次试验，要求参与者在屏幕上随机呈现（向前或向后）命令后，想象自己在隧道中向前或向后移动。三个电极双侧安装在运动皮层上。进行了带反馈的试验。使用第一阶段的数据离线分析分类器并计算两个任务的阈值。这些阈值用于形成控制信号，这些信号后来在线用于第二阶段的神经反馈训练，以触发虚拟隧道按照用户脑信号请求的方向移动。经过 96 分钟的训练，在线频带功率神经反馈训练达到了 76%的平均分类准确率，而使用功率谱密度不对称比和 AR 模型频带功率作为特征，使用 LDA 和 SVM 作为分类器的离线 BCI 模拟达到了 80%的平均分类准确率。
