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Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network
Practical brain–computer interface (BCI) demands the learning-based adaptive model that can handle diverse problems. To implement a BCI, usually functional near-infrared spectroscopy (fNIR) is used for measuring functional changes in brain oxygenation and electroencephalography (EEG) for evaluating...
Tallennettuna:
| Julkaisussa: | Health Inf Sci Syst |
|---|---|
| Päätekijät: | , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
Springer International Publishing
2019
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6790205/ https://ncbi.nlm.nih.gov/pubmed/31656595 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s13755-019-0081-5 |
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