Xavier Fidêncio Aline, Grün Felix, Klaes Christian, Iossifidis Ioannis
Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, Bochum, Germany.
Robotics and BCI Laboratory, Institute of Computer Science, Ruhr West University of Applied Sciences, Mülheim an der Ruhr, Germany.
Front Hum Neurosci. 2025 Jun 4;19:1569411. doi: 10.3389/fnhum.2025.1569411. eCollection 2025.
Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.
脑机接口（BCIs）为运动功能障碍者提供了替代性的交流方式，旨在通过外部设备控制来提高他们的生活质量。然而，使用脑电图（EEG）的非侵入性脑机接口常常由于心理状态或设备特性变化引起的非平稳性而受到性能限制。应对这些挑战推动了能够进行实时调整的自适应系统的发展。本研究探讨了一种新颖的方法，即使用强化学习（RL）创建基于自适应错误相关电位（ErrP）的脑机接口，以动态适应脑电图信号变化。该框架通过在一个公开可用的运动想象数据集上进行实验以及一个旨在提高用户参与度的新颖快节奏协议得到了验证。结果表明，强化学习智能体有效地从用户交互中学习控制策略，在各个数据集上保持了稳健的性能。然而，基于游戏的协议的结果显示，快节奏的运动想象任务对大多数参与者无效，凸显了实时脑机接口任务设计中的关键挑战。总体而言，结果证明了强化学习在增强脑机接口适应性方面的潜力，同时也识别出了任务复杂性和用户响应性方面的实际限制。
