EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover
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arXiv
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| Format: | Preprint |
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2026
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| author | Alosaimi, Ghadah Alsayyari, Maha Sun, Yixin Katsigiannis, Stamos Atapour-Abarghouei, Amir Breckon, Toby P. |
| author_facet | Alosaimi, Ghadah Alsayyari, Maha Sun, Yixin Katsigiannis, Stamos Atapour-Abarghouei, Amir Breckon, Toby P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20041 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover Alosaimi, Ghadah Alsayyari, Maha Sun, Yixin Katsigiannis, Stamos Atapour-Abarghouei, Amir Breckon, Toby P. Robotics Computer Vision and Pattern Recognition 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. |
| title | EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.20041 |