Abstract
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.
Abstract (translated)
脑机接口（BCI）为移动机器人提供了一种免手持控制方式，但在现实世界导航中解码用户意图仍然具有挑战性。本研究提出了一种用于在机器人漫游操作期间离线解码驾驶指令的脑-机器人控制系统框架。12名参与者使用操纵杆远程操控一个4WD Rover Pro平台，并沿着预定义路线执行前进、后退、左转、右转和停止等命令，同时记录了这些动作相关的脑电图（EEG）信号。这些信号通过佩戴16通道的OpenBCI帽采集，在每个运动行为发生时（Δ=0毫秒）以及未来的预测时间点（Δ>0毫秒）进行同步。 经过预处理后，研究人员评估了几种深度学习模型的效果，包括卷积神经网络、循环神经网络和Transformer架构。其中ShallowConvNet在动作预测和意图预测方面表现最佳。通过将现实世界的机器人控制与多步长EEG意图解码相结合，该研究引入了一个可重复的基准测试，并揭示了基于预测深度学习的BCI系统的关键设计见解。
URL
https://arxiv.org/abs/2602.20041
