Visual-Electrotactile Stimulation Feedback to Improve Immersive Brain-Computer Interface Based on Hand Motor Imagery
| Autor: | Shin-ichi Izumi, David Achanccaray, Mitsuhiro Hayashibe |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Article Subject General Computer Science General Mathematics medicine.medical_treatment media_common.quotation_subject Computer applications to medicine. Medical informatics 0206 medical engineering R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry 02 engineering and technology Virtual reality Premotor cortex 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Motor imagery Perception medicine Stroke media_common Brain–computer interface Rehabilitation Proprioception General Neuroscience General Medicine medicine.disease 020601 biomedical engineering medicine.anatomical_structure Psychology 030217 neurology & neurosurgery RC321-571 |
| Zdroj: | Computational Intelligence and Neuroscience, Vol 2021 (2021) |
| ISSN: | 1687-5273 1687-5265 |
| DOI: | 10.1155/2021/8832686 |
| Popis: | In the aging society, the number of people suffering from vascular disorders is rapidly increasing and has become a social problem. The death rate due to stroke, which is the second leading cause of global mortality, has increased by 40% in the last two decades. Stroke can also cause paralysis. Of late, brain-computer interfaces (BCIs) have been garnering attention in the rehabilitation field as assistive technology. A BCI for the motor rehabilitation of patients with paralysis promotes neural plasticity, when subjects perform motor imagery (MI). Feedback, such as visual and proprioceptive, influences brain rhythm modulation to contribute to MI learning and motor function restoration. Also, virtual reality (VR) can provide powerful graphical options to enhance feedback visualization. This work aimed to improve immersive VR-BCI based on hand MI, using visual-electrotactile stimulation feedback instead of visual feedback. The MI tasks include grasping, flexion/extension, and their random combination. Moreover, the subjects answered a system perception questionnaire after the experiments. The proposed system was evaluated with twenty able-bodied subjects. Visual-electrotactile feedback improved the mean classification accuracy for the grasping (93.00% ± 3.50%) and flexion/extension (95.00% ± 5.27%) MI tasks. Additionally, the subjects achieved an acceptable mean classification accuracy (maximum of 86.5% ± 5.80%) for the random MI task, which required more concentration. The proprioceptive feedback maintained lower mean power spectral density in all channels and higher attention levels than those of visual feedback during the test trials for the grasping and flexion/extension MI tasks. Also, this feedback generated greater relative power in the μ -band for the premotor cortex, which indicated better MI preparation. Thus, electrotactile stimulation along with visual feedback enhanced the immersive VR-BCI classification accuracy by 5.5% and 4.5% for the grasping and flexion/extension MI tasks, respectively, retained the subject’s attention, and eased MI better than visual feedback alone. |
| Databáze: | OpenAIRE |
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