A Real-Time BCI for Stroke Hand Rehabilitation Using Latent EEG Features from Healthy Subjects
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918163116982272 |
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| author | Omar, F. M. Omar, A. M. Eyada, K. H. Rabie, M. Kamel, M. A. Azab, A. M. |
| author_facet | Omar, F. M. Omar, A. M. Eyada, K. H. Rabie, M. Kamel, M. A. Azab, A. M. |
| contents | This study presents a real-time, portable brain-computer interface (BCI) system designed to support hand rehabilitation for stroke patients. The system combines a low cost 3D-printed robotic exoskeleton with an embedded controller that converts brain signals into physical hand movements. EEG signals are recorded using a 14-channel Emotiv EPOC+ headset and processed through a supervised convolutional autoencoder (CAE) to extract meaningful latent features from single-trial data. The model is trained on publicly available EEG data from healthy individuals (WAY-EEG-GAL dataset), with electrode mapping adapted to match the Emotiv headset layout. Among several tested classifiers, Ada Boost achieved the highest accuracy (89.3%) and F1-score (0.89) in offline evaluations. The system was also tested in real time on five healthy subjects, achieving classification accuracies between 60% and 86%. The complete pipeline - EEG acquisition, signal processing, classification, and robotic control - is deployed on an NVIDIA Jetson Nano platform with a real-time graphical interface. These results demonstrate the system's potential as a low-cost, standalone solution for home-based neurorehabilitation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Real-Time BCI for Stroke Hand Rehabilitation Using Latent EEG Features from Healthy Subjects Omar, F. M. Omar, A. M. Eyada, K. H. Rabie, M. Kamel, M. A. Azab, A. M. Human-Computer Interaction Artificial Intelligence Signal Processing This study presents a real-time, portable brain-computer interface (BCI) system designed to support hand rehabilitation for stroke patients. The system combines a low cost 3D-printed robotic exoskeleton with an embedded controller that converts brain signals into physical hand movements. EEG signals are recorded using a 14-channel Emotiv EPOC+ headset and processed through a supervised convolutional autoencoder (CAE) to extract meaningful latent features from single-trial data. The model is trained on publicly available EEG data from healthy individuals (WAY-EEG-GAL dataset), with electrode mapping adapted to match the Emotiv headset layout. Among several tested classifiers, Ada Boost achieved the highest accuracy (89.3%) and F1-score (0.89) in offline evaluations. The system was also tested in real time on five healthy subjects, achieving classification accuracies between 60% and 86%. The complete pipeline - EEG acquisition, signal processing, classification, and robotic control - is deployed on an NVIDIA Jetson Nano platform with a real-time graphical interface. These results demonstrate the system's potential as a low-cost, standalone solution for home-based neurorehabilitation. |
| title | A Real-Time BCI for Stroke Hand Rehabilitation Using Latent EEG Features from Healthy Subjects |
| topic | Human-Computer Interaction Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2510.15890 |