Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface

Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface

In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI)...

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Bibliographic Details
Main Authors: Haroon Khan, Rabindra Khadka, Malik Shahid Sultan, Anis Yazidi, Hernando Ombao, Peyman Mirtaheri
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Human Neuroscience
Subjects:
functional near-infrared spectroscopy (fNIRS)
deep learning
individual finger movements
classification
brain computer-interface (BCI)
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2024.1354143/full

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