Long-Term Mutual Training for the CYBATHLON BCI Race With a Tetraplegic Pilot: A Case Study on Inter-Session Transfer and Intra-Session Adaptation - Hehenberger, Lea; Kobler, Reinmar J.; Lopes-Dias, Catarina; Srisrisawang, Nitikorn; Tumfart, Peter; Uroko, John B.; Torke, Paul R.; Müller-Putz, Gernot R. (Frontiers in Human Neuroscience, 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 a potential end-user, 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 15 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 15 months, we recorded 26 game-based training sessions. Between the first 10 sessions, and the final 6 sessions leading up to the CYBATHLON 2020 Global Edition, the runtimes improved from 255 ± 23 s (mean ± std) to 223 ± 21 s, respectively. Moreover, we observed a concurrent increase in the classifier's accuracy from 46% to 53%, and an enhancement of the brain patterns in two classes. 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 contributed to an intensification of 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.