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%.

中文翻译：

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虚拟现实中向前/向后导航的BCI仿真和神经反馈之间的比较。

脑机接口（BCI）解码表示希望做某事的大脑信号，并将这些信号转换为控制命令。但是，先前仅对有限数量的心理任务进行了调查和分类。这项研究旨在研究使用少量EEG通道的两个运动图像（MI）命令，向前和向后移动，以用于神经反馈环境。这项研究还旨在模拟BCI，并使用不同的特征和分类方法来研究MI向前和向后运动之间的离线分类。十名健康的人参加了为期两节（每节48分钟）的实验。该实验研究了虚拟隧道中导航的神经反馈。每个环节包括320个试验，受试者被要求想象自己在屏幕上随机显示（向前或向后）命令后向前或向后运动的过程。三个电极在运动皮层两侧安装。进行了有反馈的试验。离线分析了来自会话1的数据，以训练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，该信号随后在会话2中在神经反馈训练中在线使用，以触发虚拟隧道沿用户大脑信号所要求的方向移动。经过96分钟的训练，在线乐队力量神经反馈训练的平均分类达到了76％，