Brain-computer interfaces (BCI) have the potential to provide transformative control in prosthetics, assistive technologies (wheelchairs), robotics, and human-computer interfaces. While Motor Imagery (MI) offers an intuitive approach to BCI control, its practical implementation is often limited by the requirement for expensive devices, extensive training data, and complex algorithms, leading to user fatigue and reduced accessibility. In this paper, we demonstrate that effective MI-BCI control of a mobile robot in real-world settings can be achieved using a fine-tuned Deep Neural Network (DNN) with a sliding window, eliminating the need for complex feature extractions for real-time robot control. The fine-tuning process optimizes the convolutional and attention layers of the DNN to adapt to each user's daily MI data streams, reducing training data by 70% and minimizing user fatigue from extended data collection. Using a low-cost (~$3k), 16-channel, non-invasive, open-source electroencephalogram (EEG) device, four users teleoperated a quadruped robot over three days. The system achieved 78% accuracy on a single-day validation dataset and maintained a 75% validation accuracy over three days without extensive retraining from day-to-day. For real-world robot command classification, we achieved an average of 62% accuracy. By providing empirical evidence that MI-BCI systems can maintain performance over multiple days with reduced training data to DNN and a low-cost EEG device, our work enhances the practicality and accessibility of BCI technology. This advancement makes BCI applications more feasible for real-world scenarios, particularly in controlling robotic systems.

翻译：脑机接口（BCI）在假肢、辅助技术（轮椅）、机器人学以及人机交互领域具有变革性的控制潜力。尽管运动想象（MI）为BCI控制提供了直观的方法，但其实际应用常受限于对昂贵设备、大量训练数据和复杂算法的需求，导致用户疲劳并降低可及性。本文证明，通过采用带滑动窗口的微调深度神经网络（DNN），无需复杂特征提取即可实现现实场景中移动机器人的有效MI-BCI实时控制。该微调过程优化了DNN的卷积层与注意力层，使其适配每位用户每日的MI数据流，将训练数据需求减少70%，并缓解了因长时间数据采集导致的用户疲劳。利用低成本（约3000美元）、16通道、非侵入式开源脑电图（EEG）设备，四位用户在三天内对四足机器人进行了遥操作。系统在单日验证数据集上达到78%的准确率，并在无需逐日大规模重训练的情况下，保持了三日内75%的验证准确率。在实际机器人指令分类任务中，系统平均准确率达到62%。本研究通过实证表明：基于低成本EEG设备并减少DNN训练数据的MI-BCI系统能够在多日内保持性能，从而提升了BCI技术的实用性与可及性。这一进展使得BCI应用（特别是在机器人系统控制领域）更适用于现实场景。