Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI

Hong, Keum-Shik; Naseer, Noman; Kim, Yun-Hee

doi:10.1016/j.neulet.2014.12.029

Cited by 276EEG and Brain-Computer InterfacesOptical Imaging and Spectroscopy TechniquesGaze Tracking and Assistive Technology

Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI

Hong, Keum-Shik, Naseer, Noman, Kim, Yun-Hee·2015·Neuroscience Letters

DOI 10.1016/j.neulet.2014.12.029

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Abstract

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2-7s time window during the a 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.

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Current BCI systems do not have ability to decode movement intention, instead use imagination based tasks for control.
“The current BCI systems do not have the ability to decode movement intention, instead they use imagination based tasks for control (Hong and Nguyen, 2014; Khan et al., 2014; Hong et al., 2015).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Hong et al., 2015

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Authors: Hong, K.-S., Naseer, N., & Kim, Y.-H.
Affiliations: Pusan National University (KR)
Samsung Medical Center (KR)
Corresponding author: Hong, Keum-Shik
ORCID: Hong, Keum-Shik
Naseer, Noman
Kim, Yun-Hee
Venue: Neuroscience Letters
Publisher: Elsevier BV
Published: 2015-02
Volume: 587
Pages: 87-92
ISSN: 0304-3940 (ISSN-L)
DOI: 10.1016/j.neulet.2014.12.029
OpenAlex: W2066303625
Cited by: 276
References: 42
Citations by year: 2026: 4 · 2025: 8 · 2024: 10 · 2023: 14 · 2022: 17 · 2021: 32 · 2020: 36 · 2019: 30 · 2018: 43 · 2017: 28 · 2016: 28 · 2015: 26
FWCI: 15.24
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Neuroscience Letters (publishedVersion)
PubMed (publishedVersion)
Funders: National Research Foundation of Korea under the Ministry of Science, ICT and Future Planning (NRF-2014R1A2A1A10049727, NRF-2014R1A2A1A01005128)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords: functional near-infrared spectroscopy, brain-computer interface, prefrontal cortex, primary motor cortex, motor imagery, mental arithmetic, three-class classification, linear discriminant analysis, continuous-wave NIRS
MeSH terms: Adult, Feasibility Studies, Humans, Imagination, Functional Laterality, Male, Motor Activity, Motor Cortex, Problem Solving, Discriminant Analysis, Linear Models, Prefrontal Cortex, Spectroscopy, Near-Infrared, Brain-Computer Interfaces

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Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI

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