Intelligent Systems Research Centre, Ulster University, Derry, UK.
National Rehabilitation Hospital of Ireland, Dun Laoghaire, Ireland.
J Neuroeng Rehabil. 2022 Sep 6;19(1):95. doi: 10.1186/s12984-022-01073-9.
The brain-computer interface (BCI) race at the Cybathlon championship, for people with disabilities, challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who has tetraplegia and was trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events.
A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery of left arm, right arm, and feet), and relaxed state. Three game paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon Race: BrainDriver. An evaluation of the pilot's performance is presented for two Cybathlon competition training periods-spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition.
Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy > 90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274-156 s (255 ± 24 s to 191 ± 14 s mean ± std), over 17 days (10 sessions) in 2019, and from 230-168 s (214 ± 14 s to 181 ± 4 s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly.
The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Changes in cognitive state as a result of stress, arousal level, and fatigue, associated with the competition challenge and performance pressure, were likely contributing factors to the non-stationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration not registered.
残疾人 Cybathlon 锦标赛上的脑机接口（BCI）竞赛要求参赛团队（BCI 研究人员、开发人员和脊髓损伤飞行员）在不移动的情况下，通过虚拟赛道上的虚拟化身来控制比赛。在这里，我们描述了一位患有四肢瘫痪的阿尔斯特大学 BCI 团队飞行员的训练方案和结果，他在过去 10 年中接受了基于脑电图（EEG）的 BCI 间歇性训练，以参加三项 Cybathlon 赛事。
使用多类、多二进制分类器框架来解码三个运动想象的运动（左臂、右臂和脚部的运动想象）和放松状态。使用三种游戏范式进行训练，即 NeuroSensi、Triad 和 Cybathlon Race: BrainDriver。本文介绍了在 2019 年比赛前的 5 周内进行的 20 次训练和 2020 年比赛前的 5 周内进行的 25 次训练期间，对飞行员表现的评估。
在 2009 年参加了 BCI 训练并参加了 2016 年的 Cybathlon 比赛后，这位经验丰富的飞行员在 2019 年开始训练时在所有的类对中都达到了较高的双类精度（解码精度>90%，导致高效的 NeuroSensi 和 Triad 游戏控制）。在训练期间，BrainDriver 的性能（即 Cybathlon 比赛完成时间）显著提高，从 2019 年的 17 天（10 次训练）中的 274-156 秒（255±24 秒）到 191±14 秒（17 天），从 230-168 秒（214±14 秒）到 181±4 秒（18 天），在 2020 年的 13 次训练中。然而，在两次比赛中，随着比赛日期的临近，性能都显著恶化。
所应用的训练方案和框架非常有效地实现了有竞争力的比赛完成时间。BCI 框架无法应对在比赛日前后的训练中观察到的脑电图（EEG）的显著偏差。由于比赛挑战和表现压力引起的应激、唤醒水平和疲劳导致的认知状态的变化，可能是导致 BCI 和飞行员在比赛日表现不佳的非平稳效应的促成因素。未注册试验注册。
