On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910364815327232 |
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| author | Choi, Ho Jin Das, Satyajeet Peng, Shaoting Bajcsy, Ruzena Figueroa, Nadia |
| author_facet | Choi, Ho Jin Das, Satyajeet Peng, Shaoting Bajcsy, Ruzena Figueroa, Nadia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08149 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control Choi, Ho Jin Das, Satyajeet Peng, Shaoting Bajcsy, Ruzena Figueroa, Nadia Robotics 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. |
| title | On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.08149 |