Aliakbaryhosseinabadi S, Mrachacz-Kersting N
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3188-3191. doi: 10.1109/EMBC44109.2020.9175997.
The users' mental state such as attention variations can have an effect on the brain-computer interface (BCI) performance. In this project, we implemented an adaptive online BCI system with alterations in the users' attention. Twelve electroencephalography (EEG) signals were obtained from six patients with Amyotrophic Lateral Sclerosis (ALS). Participants were asked to execute 40 trials of ankle dorsiflexion concurrently with an auditory oddball task. EEG channels, classifiers and features with superior offline performance in the training phase of the classification of attention level were selected to use in the online mode for prediction the attention status. A feedback was provided to the users to reduce the amount of attention diversion created by the oddball task. The findings revealed that the users' attention can control an online BCI system and real-time neurofeedback can be applied to focus the attention of the user back onto the main task.
用户的心理状态，如注意力变化，会对脑机接口（BCI）的性能产生影响。在本项目中，我们实现了一个随用户注意力变化的自适应在线BCI系统。从6例肌萎缩侧索硬化症（ALS）患者中获取了12个脑电图（EEG）信号。参与者被要求在执行听觉Oddball任务的同时进行40次踝背屈试验。在注意力水平分类的训练阶段，选择离线性能优越的EEG通道、分类器和特征用于在线模式，以预测注意力状态。向用户提供反馈，以减少Oddball任务造成的注意力分散。研究结果表明，用户的注意力可以控制在线BCI系统，并且实时神经反馈可用于将用户的注意力重新集中到主要任务上。
