用脑电波预测机器人越野车操作意图，12人实测有效
EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover
- 用16通道脑电设备采集驾驶时脑信号，结合不同时间延迟分析
- 浅层卷积网络在动作与意图预测中表现最优，准确率超其他模型
- 为脑机接口远程控制提供可复现的基准，适合做神经控制研究
脑机接口（BCI）为移动机器人提供了免手控制方式，但在真实场景下解码用户驾驶意图仍具挑战。本研究构建了一个离线脑-机器人控制框架，用于解码越野车操作中的驾驶指令。12名参与者通过操纵杆远程操控4WD Rover Pro平台，执行前进、后退、左转、右转和停止五类命令。使用16通道OpenBCI脑电帽采集脑电信号，并与运动行为在Δ=0毫秒及未来预测时间窗（Δ>0毫秒）对齐。经过预处理后，对比了多种深度学习模型，包括卷积神经网络、循环神经网络和Transformer架构。结果表明，ShallowConvNet在动作预测与意图预测任务中均表现最佳。该研究结合真实机器人控制与多时域脑电意图解码，建立了一个可复现的基准，揭示了基于预测深度学习的脑机接口系统的关键设计洞见。
原文摘要 · Abstract (English)
Brain-computer interfaces (BCIs) provide a hands-free control modality for mobile robotics, yet decoding user intent during real-world navigation remains challenging. This work presents a brain-robot control framework for offline decoding of driving commands during robotic rover operation. A 4WD Rover Pro platform was remotely operated by 12 participants who navigated a predefined route using a joystick, executing the commands forward, reverse, left, right, and stop. Electroencephalogram (EEG) signals were recorded with a 16-channel OpenBCI cap and aligned with motor actions at Delta = 0 ms and future prediction horizons (Delta > 0 ms). After preprocessing, several deep learning models were benchmarked, including convolutional neural networks, recurrent neural networks, and Transformer architectures. ShallowConvNet achieved the highest performance for both action prediction and intent prediction. By combining real-world robotic control with multi-horizon EEG intention decoding, this study introduces a reproducible benchmark and reveals key design insights for predictive deep learning-based BCI systems.
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