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EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover

Ghadah Alosaimi, Maha Alsayyari, Yixin Sun, Stamos Katsigiannis, Amir Atapour-Abarghouei, Toby P. Breckon

TL;DR

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

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.

EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover

TL;DR

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

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.
Paper Structure (23 sections, 1 equation, 4 figures, 2 tables, 1 algorithm)

This paper contains 23 sections, 1 equation, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Related Work
  3. Intention Decoding in Driving Scenarios
  4. Real-world vs. Simulated Driving Scenarios
  5. Deep Learning for EEG Decoding
  6. Materials and Methods
  7. Participants
  8. Experimental Setup
  9. Data Labelling
  10. EEG Data Preparation Pipeline
  11. Preprocessing
  12. Temporal-stratified splitting
  13. Model Training and Benchmarking
  14. Evaluation Metrics
  15. Results and Discussion
  16. ...and 8 more sections

Figures (4)

  • Figure 1: Experimental setup for EEG-based driver intention prediction. EEG signals were acquired from the subject and synchronised with a first-person view of the route. Commands were sent via a controller and recorded for data labelling. The rover was equipped with a ZED stereo camera, GNSS antenna, Lux, and IMU sensors to capture multimodal information during the experimental route. The recorded data were processed through preparation and labelling pipelines before being used for driver intention decoding.
  • Figure 2: Aggregated confusion matrices for (a) ShallowConvNet, (b) GRU, and (c) EEGConformer, at $\Delta = 0$ ms.
  • Figure 3: Aggregated confusion matrices for (a) ShallowConvNet, (b) GRU, and (c) EEGConformer, at $\Delta = 300$ ms.
  • Figure 4: F1-scores across all labelling horizons ($\Delta \in [0,1000]$ ms) for all examined models.