Hehenberger Lea, Kobler Reinmar J, Lopes-Dias Catarina, Srisrisawang Nitikorn, Tumfart Peter, Uroko John B, Torke Paul R, Müller-Putz Gernot R
Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
Graz BCI Racing Team Mirage 91, Graz University of Technology, Graz, Austria.
Front Hum Neurosci. 2021 Feb 26;15:635777. doi: 10.3389/fnhum.2021.635777. eCollection 2021.
CYBATHLON is an international championship where people with severe physical disabilities compete with the aid of state-of-the-art assistive technology. In one of the disciplines, the BCI Race, tetraplegic pilots compete in a computer game race by controlling an avatar with a brain-computer interface (BCI). This competition offers a perfect opportunity for BCI researchers to study long-term training effects in potential end-users, and to evaluate BCI performance in a realistic environment. In this work, we describe the BCI system designed by the team Mirage91 for participation in the CYBATHLON BCI Series 2019, as well as in the CYBATHLON 2020 Global Edition. Furthermore, we present the BCI's interface with the game and the main methodological strategies, along with a detailed evaluation of its performance over the course of the training period, which lasted 14 months. The developed system was a 4-class BCI relying on task-specific modulations of brain rhythms. We implemented inter-session transfer learning to reduce calibration time, and to reinforce the stability of the brain patterns. Additionally, in order to compensate for potential intra-session shifts in the features' distribution, normalization parameters were continuously adapted in an unsupervised fashion. Across the aforementioned 14 months, we recorded 26 game-based training sessions. Between the first eight sessions, and the final eight sessions leading up to the CYBATHLON 2020 Global Edition, the runtimes significantly improved from 255 ± 23 s (mean ± std) to 225 ± 22 s, respectively. Moreover, we observed a significant increase in the classifier's accuracy from 46 to 53%, driven by more distinguishable brain patterns. Compared to conventional single session, non-adaptive BCIs, the inter-session transfer learning and unsupervised intra-session adaptation techniques significantly improved the performance. This long-term study demonstrates that regular training helped the pilot to significantly increase the distance between task-specific patterns, which resulted in an improvement of performance, both with respect to class separability in the calibration data, and with respect to the game. Furthermore, it shows that our methodological approaches were beneficial in transferring the performance across sessions, and most importantly to the CYBATHLON competitions.
“赛博athlon”是一项国际锦标赛，重度身体残疾者借助最先进的辅助技术参与其中。在其中一项赛事——脑机接口竞赛中，四肢瘫痪的选手通过脑机接口（BCI）控制虚拟角色，参与电脑游戏竞赛。这项比赛为BCI研究人员提供了一个绝佳机会，用以研究潜在终端用户的长期训练效果，并在现实环境中评估BCI的性能。在这项工作中，我们描述了Mirage91团队为参加2019年“赛博athlon”脑机接口系列赛以及2020年“赛博athlon”全球版而设计的脑机接口系统。此外，我们展示了该脑机接口与游戏的交互界面以及主要方法策略，并对其在为期14个月的训练期间的性能进行了详细评估。所开发的系统是一个基于脑节律特定任务调制的四类脑机接口。我们实施了跨时段迁移学习，以减少校准时间并增强脑电模式的稳定性。此外，为了补偿特征分布在时段内的潜在变化，归一化参数以无监督方式持续调整。在上述14个月期间，我们记录了26次基于游戏的训练课程。在2020年“赛博athlon”全球版之前的前八次课程与最后八次课程之间，运行时间分别从255±23秒（均值±标准差）显著提高到225±22秒。此外，我们观察到分类器的准确率从46%显著提高到53%，这得益于更具辨识度的脑电模式。与传统的单时段、非自适应脑机接口相比，跨时段迁移学习和无监督的时段内自适应技术显著提高了性能。这项长期研究表明，定期训练有助于选手显著增加特定任务模式之间的差异，这在校准数据的类别可分性以及游戏方面都带来了性能提升。此外，研究表明我们的方法策略有助于在不同时段之间迁移性能，最重要的是对“赛博athlon”竞赛有帮助。
