On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control
<|reference_start|>On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control: This paper explores the feasibility of employing EEG-based intention detection for real-time robot assistive control. We focus on predicting and distinguishing motor intentions of left/right arm movements by presenting: i) an offline data collection and training pipeline, used to train a classifier for left/right motion intention prediction, and ii) an online real-time prediction pipeline leveraging the trained classifier and integrated with an assistive robot. Central to our approach is a rich feature representation composed of the tangent space projection of time-windowed sample covariance matrices from EEG filtered signals and derivatives; allowing for a simple SVM classifier to achieve unprecedented accuracy and real-time performance. In pre-recorded real-time settings (160 Hz), a peak accuracy of 86.88% is achieved, surpassing prior works. In robot-in-the-loop settings, our system successfully detects intended motion solely from EEG data with 70% accuracy, triggering a robot to execute an assistive task. We provide a comprehensive evaluation of the proposed classifier.<|reference_end|>
@article{choi2024on,
    title={On the Feasibility of EEG-based Motor Intention Detection for Real-Time
  Robot Assistive Control},
    author={Ho Jin Choi, Satyajeet Das, Shaoting Peng, Ruzena Bajcsy and Nadia
  Figueroa},
    journal={arXiv preprint arXiv:2403.08149},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08149},
    primaryClass={cs.RO}
}