Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada.
J Neural Eng. 2019 Nov 11;16(6):066042. doi: 10.1088/1741-2552/ab4943.
The ultimate goal of many brain-computer interface (BCI) research efforts is to provide individuals with severe motor impairments with a communication channel that they can control at will. To achieve this goal, an important system requirement is asynchronous control, whereby users can initiate intentional brain activation in a self-paced rather than system-cued manner. However, to date, asynchronous BCIs have been explored in a minority of BCI studies and their performance is generally below that of system-paced alternatives. In this paper, we present an asynchronous electroencephalography (EEG) BCI that detects a non-motor imagery cognitive task and investigated the possibility of improving its performance using error-related potentials (ErrP).
Ten able-bodied adults attended two sessions of data collection each, one for training and one for testing the BCI. The visual interface consisted of a centrally located cartoon icon. For each participant, an asynchronous BCI differentiated among the idle state and a personally selected cognitive task (mental arithmetic, word generation or figure rotation). The BCI continuously analyzed the EEG data stream and displayed real-time feedback (i.e. icon fell over) upon detection of brain activity indicative of a cognitive task. The BCI also monitored the EEG signals for the presence of error-related potentials following the presentation of feedback. An ErrP classifier was invoked to automatically alter the task classifier outcome when an error-related potential was detected.
The average post-error correction trial success rate across participants, 85% [Formula: see text] 12%, was significantly higher (p < 0.05) than that pre-error correction (78% [Formula: see text] 11%).
Our findings support the addition of ErrP-correction to maximize the performance of asynchronous BCIs..
许多脑机接口 (BCI) 研究的最终目标是为严重运动障碍的个体提供一种他们可以自主控制的通信渠道。为了实现这一目标，一个重要的系统要求是异步控制，即用户可以以自我节奏而不是系统提示的方式启动有意的大脑激活。然而，迄今为止，异步 BCI 仅在少数 BCI 研究中进行了探索，其性能通常低于系统提示的替代方案。在本文中，我们提出了一种异步脑电图 (EEG) BCI，用于检测非运动想象认知任务，并研究了使用错误相关电位 (ErrP) 来提高其性能的可能性。
十名健康成年人参加了两次数据采集，一次用于训练，一次用于测试 BCI。视觉界面由一个位于中央的卡通图标组成。对于每个参与者，异步 BCI 可区分空闲状态和个人选择的认知任务（心算、生成单词或图形旋转）。BCI 连续分析 EEG 数据流，并在检测到指示认知任务的大脑活动时显示实时反馈（即图标倒下）。BCI 还会在显示反馈后监控 EEG 信号中是否存在错误相关电位。当检测到错误相关电位时，会调用 ErrP 分类器自动更改任务分类器的结果。
参与者的平均后错误校正试验成功率为 85%[公式：见正文]12%，显著高于校正前（78%[公式：见正文]11%）（p<0.05）。
我们的研究结果支持添加 ErrP 校正以最大程度地提高异步 BCI 的性能。
