| Autor: |
Dale R; 1 University of Birmingham B152TT Birmingham U.K., O'sullivan TD; 2 University of Notre Dame Notre Dame IN 46556 USA., Howard S; 2 University of Notre Dame Notre Dame IN 46556 USA., Orihuela-Espina F; 1 University of Birmingham B152TT Birmingham U.K., Dehghani H; 1 University of Birmingham B152TT Birmingham U.K. |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE open journal of engineering in medicine and biology [IEEE Open J Eng Med Biol] 2023 Mar 16; Vol. 4, pp. 85-95. Date of Electronic Publication: 2023 Mar 16 (Print Publication: 2023). |
| DOI: |
10.1109/OJEMB.2023.3248492 |
| Abstrakt: |
An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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