Academic Journal
Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training
| العنوان: | Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training |
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| المؤلفون: | Zhongpeng Wang, Lu Yang, Yijie Zhou, Long Chen, Bin Gu, Shuang Liu, Minpeng Xu, Feng He, Dong Ming |
| المصدر: | IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2872-2882 (2023) |
| بيانات النشر: | IEEE, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medical technology LCC:Therapeutics. Pharmacology |
| مصطلحات موضوعية: | Brain--computer interface, neurofeedback training, hybrid brain signal, weighted EEG-fNIRS patterns, motor imagery, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950 |
| الوصف: | As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information ( $\sim $ 10% and $\sim $ 18% improvement respectively), reaching $\sim $ 89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1558-0210 |
| Relation: | https://ieeexplore.ieee.org/document/10141658/; https://doaj.org/toc/1558-0210 |
| DOI: | 10.1109/TNSRE.2023.3281855 |
| URL الوصول: | https://doaj.org/article/41554cd6780241c79bea14720e510d8b |
| رقم الانضمام: | edsdoj.41554cd6780241c79bea14720e510d8b |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 15580210 |
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| DOI: | 10.1109/TNSRE.2023.3281855 |