A Straightforward Approach to fNIRS Channel Selection for Distinguishing Mental States from Resting States: Effective in Both Subject-Dependent and Subject-Independent Classification Models
Abstract
Purpose: Functional Near-Infrared Spectroscopy (fNIRS) is a relatively novel tool that measures local hemodynamic changes, including oxygenated hemoglobin [Oxy-Hb], deoxygenated hemoglobin [Deoxy-Hb], and total hemoglobin [Tot-Hb]. Its safety, portability, non-invasiveness, and cost-effectiveness make it a preferred technique for designing Brain-Computer Interfaces (BCIs). This study aims to develop an accurate fNIRS-based BCI module for classifying mental tasks and the resting state.
Materials and Methods: Rather than relying on conventional statistical features, our approach utilizes nonlinear indices derived from a 2D Poincaré plot. These measures are computationally efficient and capable of revealing the underlying dynamics of the system. Our primary innovation lies in the development of a novel feature and selection method. We assessed mental task recognition in both subject-dependent and subject-independent classification modes.
Results: Our findings demonstrated a maximum accuracy of 93.75% for subject-specific style and 91.67% for subject-independent style.
Conclusion: In summary, the simplicity and high performance of the proposed framework suggest promising future directions for designing online fNIRS-based BCI systems.
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[8] Noori, F. M., Naseer, N., Qureshi, N. K., Nazeer, H., & Khan, R. A. (2017). Optimal feature selection from fNIRS signals using genetic algorithms for BCI. Neuroscience letters, 647, 61–66. https://doi.org/10.1016/j.neulet.2017.03.013
[9] Li, R., Potter, T., Huang, W., & Zhang, Y. (2017). Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features. Frontiers in human neuroscience, 11, 462. https://doi.org/10.3389/fnhum.2017.00462
[10] Ghaffar, M.S.B.A., Khan, U.S., Iqbal, J., Rashid, N., Hamza, A., Qureshi, W.S., Tiwana, M.I., Izhar, U. (2021). Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC). Infrared Physics & Technology, 112, 103589. https://doi.org/10.1016/j.infrared.2020.103589.
[11] Ghouse, A., Nardelli, M., & Valenza, G. (2020). fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks. Entropy (Basel, Switzerland), 22(7), 761. https://doi.org/10.3390/e22070761
[12] Bauernfeind, G., Scherer, R., Pfurtscheller, G., & Neuper, C. (2011). Single-trial classification of antagonistic oxyhemoglobin responses during mental arithmetic. Medical & biological engineering & computing, 49(9), 979–984. https://doi.org/10.1007/s11517-011-0792-5
[13] Bauernfeind, G., Steyrl, D., Brunner, C., & Muller-Putz, G. R. (2014). Single trial classification of fNIRS-based brain-computer interface mental arithmetic data: a comparison between different classifiers. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2014, 2004–2007. https://doi.org/10.1109/EMBC.2014.6944008
[14] Kurz, E. M., Wood, G., Kober, S. E., Schippinger, W., Pichler, G., Müller-Putz, G., & Bauernfeind, G. (2018). Towards using fNIRS recordings of mental arithmetic for the detection of residual cognitive activity in patients with disorders of consciousness (DOC). Brain and cognition, 125, 78–87. https://doi.org/10.1016/j.bandc.2018.06.002
[15] Vassena, E., Gerrits, R., Demanet, J., Verguts, T., & Siugzdaite, R. (2019). Anticipation of a mentally effortful task recruits Dorsolateral Prefrontal Cortex: An fNIRS validation study. Neuropsychologia, 123, 106–115. https://doi.org/10.1016/j.neuropsychologia.2018.04.033
[16] Herff, C., Heger, D., Fortmann, O., Hennrich, J., Putze, F., & Schultz, T. (2014). Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS. Frontiers in human neuroscience, 7, 935. https://doi.org/10.3389/fnhum.2013.00935
[17] Saikia, M. J., Besio, W. G., & Mankodiya, K. (2021). The Validation of a Portable Functional NIRS System for Assessing Mental Workload. Sensors (Basel, Switzerland), 21(11), 3810. https://doi.org/10.3390/s21113810
[18] Shirzadi, S., Einalou, Z., & Dadgostar, M. (2020). Investigation of Functional Connectivity During Working Memory Task and Hemispheric Lateralization in Left- and Right- Handers Measured by fNIRS. Optik, 221: 165347. https://doi.org/10.1016/j.ijleo.2020.165347.
[19] Naito, M., Michioka, Y., Ozawa, K., Ito, Y., Kiguchi, M., Kanazawa, T. (2007). A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Transactions on Information and Systems, E90D(7), 1028-1037. doi:10.1093/ietisy/e90-d.7.1028
[20] Putze, F., Hesslinger, S., Tse, C. Y., Huang, Y., Herff, C., Guan, C., & Schultz, T. (2014). Hybrid fNIRS-EEG based classification of auditory and visual perception processes. Frontiers in neuroscience, 8, 373. https://doi.org/10.3389/fnins.2014.00373
[21] Durantin, G., Gagnon, J. F., Tremblay, S., & Dehais, F. (2014). Using near infrared spectroscopy and heart rate variability to detect mental overload. Behavioural brain research, 259, 16–23. https://doi.org/10.1016/j.bbr.2013.10.042
[22] Invitto, S., Montinaro, R., Ciccarese, V., Venturella, I., Fronda, G., & Balconi, M. (2019). Smell and 3D Haptic Representation: A Common Pathway to Understand Brain Dynamics in a Cross-Modal Task. A Pilot OERP and fNIRS Study. Frontiers in behavioral neuroscience, 13, 226. https://doi.org/10.3389/fnbeh.2019.00226
[23] Abdalmalak, A., Milej, D., Norton, L., Debicki, D. B., Owen, A. M., & Lawrence, K. S. (2021). The Potential Role of fNIRS in Evaluating Levels of Consciousness. Frontiers in human neuroscience, 15, 703405. https://doi.org/10.3389/fnhum.2021.703405
[24] Midha, S., Maior, H.A., Wilson, M.L., & Sharples, S. (2021). Measuring Mental Workload Variations in Office Work Tasks using fNIRS. International Journal of Human-Computer Studies, 147, 102580. https://doi.org/10.1016/j.ijhcs.2020.102580.
[25] Holper, L., & Wolf, M. (2011). Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study. Journal of neuroengineering and rehabilitation, 8, 34. https://doi.org/10.1186/1743-0003-8-34
[26] Khan, M. J., & Hong, K. S. (2015). Passive BCI based on drowsiness detection: an fNIRS study. Biomedical optics express, 6(10), 4063–4078. https://doi.org/10.1364/BOE.6.004063
[27] Aydin E. A. (2020). Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces. Computer methods and programs in biomedicine, 195, 105535. https://doi.org/10.1016/j.cmpb.2020.105535
[28] Shin, J., von Luhmann, A., Blankertz, B., Kim, D. W., Jeong, J., Hwang, H. J., & Muller, K. R. (2017). Open Access Dataset for EEG+NIRS Single-Trial Classification. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 25(10), 1735–1745. https://doi.org/10.1109/TNSRE.2016.2628057
[29] Ergün E., & Aydemir, Ö. (2018). Decoding of Binary Mental Arithmetic Based Near-Infrared Spectroscopy Signals. 3rd International Conference on Computer Science and Engineering (UBMK), 2018, 201-204. doi: 10.1109/UBMK.2018.8566462.
[30] Jiang, X., Gu, X., Xu, K., Ren, H., & Chen, W. (2019). Independent Decision Path Fusion for Bimodal Asynchronous Brain–Computer Interface to Discriminate Multiclass Mental States. IEEE Access, 7, 165303-165317. doi: 10.1109/ACCESS.2019.2953535.
[31] Nguyen, H. D., & Hong, K. S. (2016). Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy. Biomedical optics express, 7(9), 3491–3507. https://doi.org/10.1364/BOE.7.003491
[32] Naseer, N., Noori, F. M., Qureshi, N. K., & Hong, K. S. (2016). Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application. Frontiers in human neuroscience, 10, 237. https://doi.org/10.3389/fnhum.2016.00237
[33] Goshvarpour A, Goshvarpour A. Matching pursuit-based analysis of fNIRS in combination with cascade PCA and reliefF for mental task recognition. Expert Systems with Applications 2023; 213(Part C): 119283. https://doi.org/10.1016/j.eswa.2022.119283
[34] Khandoker AH, Karmakar C, Brennan M, Palaniswami M, Voss A. Poincaré Plot Methods for Heart Rate Variability Analysis. Springer New York, NY; 2013. ISBN: 978-1-4614-7374-9.
[35] Barahimi, S., Einalou, Z., & Dadgostar, M. (2021). Evaluation of hemodynamic response function during mental arithmetic task in fNIRS data using GLM method, Neuroscience Informatics, 1 (1-2), 100004. https://doi.org/10.1016/j.neuri.2021.100004
[36] Liu S. Applying antagonistic activation pattern to the single-trial classification of mental arithmetic. Heliyon 2022;8:e11102
[37] Guo Z, Chen F. Impacts of simplifying articulation movements imagery to speech imagery BCI performance. J. Neural Eng. 2023 (in press) https://doi.org/10.1088/1741-2552/acb232
[2] van Gerven M, Farquhar J, Schaefer R, Vlek R, Geuze J, Nijholt A, Ramsey N, Haselager P, Vuurpijl L, Gielen S, Desain P. The brain-computer interface cycle. J Neural Eng. 2009 Aug;6(4):041001. doi: 10.1088/1741-2560/6/4/041001.
[3] Irani, F., Platek, S. M., Bunce, S., Ruocco, A. C., & Chute, D. (2007). Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders. The Clinical neuropsychologist, 21(1), 9–37. https://doi.org/10.1080/13854040600910018
[4] Naseer, N., & Hong, K. S. (2015). fNIRS-based brain-computer interfaces: a review. Frontiers in human neuroscience, 9, 3. https://doi.org/10.3389/fnhum.2015.00003
[5] Beisteiner, R., Höllinger, P., Lindinger, G., Lang, W., & Berthoz, A. (1995). Mental representations of movements. Brain potentials associated with imagination of hand movements. Electroencephalography and clinical neurophysiology, 96(2), 183–193. https://doi.org/10.1016/0168-5597(94)00226-5
[6] Sitaram, R., Zhang, H., Guan, C., Thulasidas, M., Hoshi, Y., Ishikawa, A., Shimizu, K., & Birbaumer, N. (2007). Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. NeuroImage, 34(4), 1416–1427. https://doi.org/10.1016/j.neuroimage.2006.11.005
[7] Hong, K. S., Naseer, N., & Kim, Y. H. (2015). Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI. Neuroscience letters, 587, 87–92. https://doi.org/10.1016/j.neulet.2014.12.029
[8] Noori, F. M., Naseer, N., Qureshi, N. K., Nazeer, H., & Khan, R. A. (2017). Optimal feature selection from fNIRS signals using genetic algorithms for BCI. Neuroscience letters, 647, 61–66. https://doi.org/10.1016/j.neulet.2017.03.013
[9] Li, R., Potter, T., Huang, W., & Zhang, Y. (2017). Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features. Frontiers in human neuroscience, 11, 462. https://doi.org/10.3389/fnhum.2017.00462
[10] Ghaffar, M.S.B.A., Khan, U.S., Iqbal, J., Rashid, N., Hamza, A., Qureshi, W.S., Tiwana, M.I., Izhar, U. (2021). Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC). Infrared Physics & Technology, 112, 103589. https://doi.org/10.1016/j.infrared.2020.103589.
[11] Ghouse, A., Nardelli, M., & Valenza, G. (2020). fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks. Entropy (Basel, Switzerland), 22(7), 761. https://doi.org/10.3390/e22070761
[12] Bauernfeind, G., Scherer, R., Pfurtscheller, G., & Neuper, C. (2011). Single-trial classification of antagonistic oxyhemoglobin responses during mental arithmetic. Medical & biological engineering & computing, 49(9), 979–984. https://doi.org/10.1007/s11517-011-0792-5
[13] Bauernfeind, G., Steyrl, D., Brunner, C., & Muller-Putz, G. R. (2014). Single trial classification of fNIRS-based brain-computer interface mental arithmetic data: a comparison between different classifiers. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2014, 2004–2007. https://doi.org/10.1109/EMBC.2014.6944008
[14] Kurz, E. M., Wood, G., Kober, S. E., Schippinger, W., Pichler, G., Müller-Putz, G., & Bauernfeind, G. (2018). Towards using fNIRS recordings of mental arithmetic for the detection of residual cognitive activity in patients with disorders of consciousness (DOC). Brain and cognition, 125, 78–87. https://doi.org/10.1016/j.bandc.2018.06.002
[15] Vassena, E., Gerrits, R., Demanet, J., Verguts, T., & Siugzdaite, R. (2019). Anticipation of a mentally effortful task recruits Dorsolateral Prefrontal Cortex: An fNIRS validation study. Neuropsychologia, 123, 106–115. https://doi.org/10.1016/j.neuropsychologia.2018.04.033
[16] Herff, C., Heger, D., Fortmann, O., Hennrich, J., Putze, F., & Schultz, T. (2014). Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS. Frontiers in human neuroscience, 7, 935. https://doi.org/10.3389/fnhum.2013.00935
[17] Saikia, M. J., Besio, W. G., & Mankodiya, K. (2021). The Validation of a Portable Functional NIRS System for Assessing Mental Workload. Sensors (Basel, Switzerland), 21(11), 3810. https://doi.org/10.3390/s21113810
[18] Shirzadi, S., Einalou, Z., & Dadgostar, M. (2020). Investigation of Functional Connectivity During Working Memory Task and Hemispheric Lateralization in Left- and Right- Handers Measured by fNIRS. Optik, 221: 165347. https://doi.org/10.1016/j.ijleo.2020.165347.
[19] Naito, M., Michioka, Y., Ozawa, K., Ito, Y., Kiguchi, M., Kanazawa, T. (2007). A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Transactions on Information and Systems, E90D(7), 1028-1037. doi:10.1093/ietisy/e90-d.7.1028
[20] Putze, F., Hesslinger, S., Tse, C. Y., Huang, Y., Herff, C., Guan, C., & Schultz, T. (2014). Hybrid fNIRS-EEG based classification of auditory and visual perception processes. Frontiers in neuroscience, 8, 373. https://doi.org/10.3389/fnins.2014.00373
[21] Durantin, G., Gagnon, J. F., Tremblay, S., & Dehais, F. (2014). Using near infrared spectroscopy and heart rate variability to detect mental overload. Behavioural brain research, 259, 16–23. https://doi.org/10.1016/j.bbr.2013.10.042
[22] Invitto, S., Montinaro, R., Ciccarese, V., Venturella, I., Fronda, G., & Balconi, M. (2019). Smell and 3D Haptic Representation: A Common Pathway to Understand Brain Dynamics in a Cross-Modal Task. A Pilot OERP and fNIRS Study. Frontiers in behavioral neuroscience, 13, 226. https://doi.org/10.3389/fnbeh.2019.00226
[23] Abdalmalak, A., Milej, D., Norton, L., Debicki, D. B., Owen, A. M., & Lawrence, K. S. (2021). The Potential Role of fNIRS in Evaluating Levels of Consciousness. Frontiers in human neuroscience, 15, 703405. https://doi.org/10.3389/fnhum.2021.703405
[24] Midha, S., Maior, H.A., Wilson, M.L., & Sharples, S. (2021). Measuring Mental Workload Variations in Office Work Tasks using fNIRS. International Journal of Human-Computer Studies, 147, 102580. https://doi.org/10.1016/j.ijhcs.2020.102580.
[25] Holper, L., & Wolf, M. (2011). Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study. Journal of neuroengineering and rehabilitation, 8, 34. https://doi.org/10.1186/1743-0003-8-34
[26] Khan, M. J., & Hong, K. S. (2015). Passive BCI based on drowsiness detection: an fNIRS study. Biomedical optics express, 6(10), 4063–4078. https://doi.org/10.1364/BOE.6.004063
[27] Aydin E. A. (2020). Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces. Computer methods and programs in biomedicine, 195, 105535. https://doi.org/10.1016/j.cmpb.2020.105535
[28] Shin, J., von Luhmann, A., Blankertz, B., Kim, D. W., Jeong, J., Hwang, H. J., & Muller, K. R. (2017). Open Access Dataset for EEG+NIRS Single-Trial Classification. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 25(10), 1735–1745. https://doi.org/10.1109/TNSRE.2016.2628057
[29] Ergün E., & Aydemir, Ö. (2018). Decoding of Binary Mental Arithmetic Based Near-Infrared Spectroscopy Signals. 3rd International Conference on Computer Science and Engineering (UBMK), 2018, 201-204. doi: 10.1109/UBMK.2018.8566462.
[30] Jiang, X., Gu, X., Xu, K., Ren, H., & Chen, W. (2019). Independent Decision Path Fusion for Bimodal Asynchronous Brain–Computer Interface to Discriminate Multiclass Mental States. IEEE Access, 7, 165303-165317. doi: 10.1109/ACCESS.2019.2953535.
[31] Nguyen, H. D., & Hong, K. S. (2016). Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy. Biomedical optics express, 7(9), 3491–3507. https://doi.org/10.1364/BOE.7.003491
[32] Naseer, N., Noori, F. M., Qureshi, N. K., & Hong, K. S. (2016). Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application. Frontiers in human neuroscience, 10, 237. https://doi.org/10.3389/fnhum.2016.00237
[33] Goshvarpour A, Goshvarpour A. Matching pursuit-based analysis of fNIRS in combination with cascade PCA and reliefF for mental task recognition. Expert Systems with Applications 2023; 213(Part C): 119283. https://doi.org/10.1016/j.eswa.2022.119283
[34] Khandoker AH, Karmakar C, Brennan M, Palaniswami M, Voss A. Poincaré Plot Methods for Heart Rate Variability Analysis. Springer New York, NY; 2013. ISBN: 978-1-4614-7374-9.
[35] Barahimi, S., Einalou, Z., & Dadgostar, M. (2021). Evaluation of hemodynamic response function during mental arithmetic task in fNIRS data using GLM method, Neuroscience Informatics, 1 (1-2), 100004. https://doi.org/10.1016/j.neuri.2021.100004
[36] Liu S. Applying antagonistic activation pattern to the single-trial classification of mental arithmetic. Heliyon 2022;8:e11102
[37] Guo Z, Chen F. Impacts of simplifying articulation movements imagery to speech imagery BCI performance. J. Neural Eng. 2023 (in press) https://doi.org/10.1088/1741-2552/acb232
| Files | ||
| Issue | Vol 13 No 1 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i1.20780 | |
| Keywords | ||
| Functional near-infrared spectroscopy; Poincare plot; Feature/channel selection; Mental calculation; Classification | ||
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How to Cite
1.
Goshvarpour A. A Straightforward Approach to fNIRS Channel Selection for Distinguishing Mental States from Resting States: Effective in Both Subject-Dependent and Subject-Independent Classification Models. Frontiers Biomed Technol. 2026;13(1):117-132.
