On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control
CoRR(2024)
摘要
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
achieved, surpassing prior works. In robot-in-the-loop settings, our system
successfully detects intended motion solely from EEG data with 70
triggering a robot to execute an assistive task. We provide a comprehensive
evaluation of the proposed classifier.
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