Exploring Brain Functional Connectivity in Hand Motion and Motor Imagery through fNIRS Signals: A Graph Theory Approach
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Abstract
Using functional near-infrared spectroscopy (fNIRS) as a complementary and cost-effective neuroimaging technique in sensorimotor tasks due to its applications in brain-computer interface (BCI) research can provide useful information about functional connectivity of brain networks. However, few studies on brain functional connectivity during sensorimotor tasks have often focused on evaluating brain activity electrically. In the present study, a signal processing algorithm using fNIRS-HbO2 data has been suggested to find active parts of the brain for motion and motor imagery in motor imagery task. In this algorithm, first, the wavelet transform was used to remove the noise and preprocess the signal. Then, using correlation analysis, functional connectivity matrices in motion and motor imagery were extracted, and finally, global efficiency (GE) values were calculated. In addition to investigating the conditions of the small-world network in the connectivity matrix, the classification of motion and motor imagery was investigated using a t-test. For this purpose, a 20-channel fNIRS signal was recorded to measure changes in HbO2 concentration in the motor cortex of 12 healthy individuals with a sampling frequency of 10 Hz. The results, in addition to confirming the presence of a small-world network in the graphs from the correlation matrix, showed that the classification of motion and motor imagery of right and left hands will be significant when 40% of the strongest connectivity between channels was selected. The results showed that in the left hemisphere there was stronger connectivity between the channels. In general, the results not only showed the activity of brain networks in performing sensorimotor tasks as small-world networks, but they also reported the role of the dominant hemisphere in performing these tasks.
1- Mehrdad Dadgostar, Seyed Kamaledin Setarehdan, Sohrab Shahzadi, and Ata Akin, "Functional connectivity of the PFC via partial correlation." Optik, Vol. 127 (No. 11), pp. 4748-54, (2016).
2- Shirley M Coyle, Tomás E Ward, and Charles M Markham, "Brain–computer interface using a simplified functional near-infrared spectroscopy system." Journal of neural engineering, Vol. 4 (No. 3), p. 219, (2007).
3- Shirley Coyle, Tomás Ward, Charles Markham, and Gary McDarby, "On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces." Physiological measurement, Vol. 25 (No. 4), p. 815, (2004).
4- Vera Kaiser et al., "Cortical effects of user training in a motor imagery based brain–computer interface measured by fNIRS and EEG." Neuroimage, Vol. 85pp. 432-44, (2014).
5- Sarah D Power, Tiago H Falk, and Tom Chau, "Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy." Journal of neural engineering, Vol. 7 (No. 2), p. 026002, (2010).
6- Sarah D Power, Azadeh Kushki, and Tom Chau, "Towards a system-paced near-infrared spectroscopy brain–computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state." Journal of neural engineering, Vol. 8 (No. 6), p. 066004, (2011).
7- Tiago H Falk, Mirna Guirgis, Sarah Power, and Tom T Chau, "Taking NIRS-BCIs outside the lab: towards achieving robustness against environment noise." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 19 (No. 2), pp. 136-46, (2010).
8- Masayoshi Naito, Yohko Michioka, Kuniaki Ozawa, Yoshitoshi Ito, Masashi Kiguchi, and Tsuneo Kanazawa, "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, Vol. 90 (No. 7), pp. 1028-37, (2007).
9- Berdakh Abibullaev and Jinung An, "Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms." Medical engineering & physics, Vol. 34 (No. 10), pp. 1394-410, (2012).
10- Mohammadreza Abtahi, Amir Mohammad Amiri, Dennis Byrd, and Kunal Mankodiya, "Hand motion detection in fNIRS neuroimaging data." in Healthcare, (2017), Vol. 5 (No. 2): MDPI, p. 20.
11- Hendrik Santosa and Keum-Shik Hong, "An fNIRS-based BCI for mental arithmetic task using ICA." in 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems, (2013): IEEE, pp. 219-23.
12- Günther Bauernfeind, David Steyrl, Clemens Brunner, and Gernot R Müller-Putz, "Single trial classification of fNIRS-based brain-computer interface mental arithmetic data: a comparison between different classifiers." in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (2014): IEEE, pp. 2004-07.
13- Gert Pfurtscheller et al., "The hybrid BCI." Frontiers in neuroscience, Vol. 4p. 3, (2010).
14- Jaeyoung Shin, Klaus-R Müller, and Han-Jeong Hwang, "Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic." Scientific reports, Vol. 6 (No. 1), pp. 1-11, (2016).
15- Larissa C Schudlo and Tom Chau, "Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest." Journal of neural engineering, Vol. 11 (No. 1), p. 016003, (2013).
16- Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, and Keum-Shik Hong, "Analysis of different classification techniques for two-class functional near-infrared spectroscopy-based brain-computer interface." Computational intelligence and neuroscience, Vol. 2016(2016).
17- Zahra Einalou, Keivan Maghooli, Seyaed Kamaledin Setarehdan, and Ata Akin, "Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS." Neurophotonics, Vol. 4 (No. 4), p. 041407, (2017).
18- Zahra Einalou, Keyvan Maghooli, Seyed-Kamaledin Setarehdan, and Ata Akin, "Functional near infrared spectroscopy to investigation of functional connectivity in schizophrenia using partial correlation." Universal Journal of Biomedical Engineering, Vol. 2 (No. 1), pp. 5-8, (2014).
19- Liwei Xu, Bitian Wang, Gongcheng Xu, Wei Wang, Zhian Liu, and Zengyong Li, "Functional connectivity analysis using fNIRS in healthy subjects during prolonged simulated driving." Neuroscience letters, Vol. 640pp. 21-28, (2017).
20- Takashi Hanakawa, Michael A Dimyan, and Mark Hallett, "Motor planning, imagery, and execution in the distributed motor network: a time-course study with functional MRI." Cerebral cortex, Vol. 18 (No. 12), pp. 2775-88, (2008).
21- Jörn Munzert, Britta Lorey, and Karen Zentgraf, "Cognitive motor processes: the role of motor imagery in the study of motor representations." Brain research reviews, Vol. 60 (No. 2), pp. 306-26, (2009).
22- Nikhil Sharma, Peter S Jones, TA Carpenter, and Jean-Claude Baron, "Mapping the involvement of BA 4a and 4p during motor imagery." Neuroimage, Vol. 41 (No. 1), pp. 92-99, (2008).
23- Ana Solodkin, Petr Hlustik, E Elinor Chen, and Steven L Small, "Fine modulation in network activation during motor execution and motor imagery." Cerebral cortex, Vol. 14 (No. 11), pp. 1246-55, (2004).
24- Jinung An et al., "Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: An fNIRS pilot study." in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2013): IEEE, pp. 6345-48.
25- Johann P Kuhtz‐Buschbeck, C Mahnkopf, C Holzknecht, H Siebner, S Ulmer, and O Jansen, "Effector‐independent representations of simple and complex imagined finger movements: A combined fMRI and TMS study." European Journal of Neuroscience, Vol. 18 (No. 12), pp. 3375-87, (2003).
26- Christoph Stippich, Henrik Ochmann, and Klaus Sartor, "Somatotopic mapping of the human primary sensorimotor cortex during motor imagery and motor execution by functional magnetic resonance imaging." Neuroscience letters, Vol. 331 (No. 1), pp. 50-54, (2002).
27- Carlo A Porro et al., "Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study." Journal of Neuroscience, Vol. 16 (No. 23), pp. 7688-98, (1996).
28- Rui Zhang, Yamin Yan, Yuxia Hu, Li Shi, and Hong Wan, "EEG function network analysis of left and right hand motor imagery." in 2017 Chinese Automation Congress (CAC), (2017): IEEE, pp. 346-50.
29- Lulu Hu et al., "The Changes of Brain Networks Topology in Graph Theory of rt-fMRI Emotion Self-regulation Training." in International Conference on Brain Informatics, (2019): Springer, pp. 126-35.
30- Ali Dejman, Ali Khadem, and Anahita Khorrami, "Exploring the disorders of brain effective connectivity network in ASD: a case study using EEG, transfer entropy, and graph theory." in 2017 Iranian Conference on Electrical Engineering (ICEE), (2017): IEEE, pp. 8-13.
31- Liang Wang et al., "Altered small‐world brain functional networks in children with attention‐deficit/hyperactivity disorder." Human brain mapping, Vol. 30 (No. 2), pp. 638-49, (2009).
32- Jessica Guilbert, François Jouen, and Michèle Molina, "Motor imagery development and proprioceptive integration: Which sensory reweighting during childhood?" Journal of experimental child psychology, Vol. 166pp. 621-34, (2018).
33- Sima Shirzadi, Mehrdad Dadgostar, Hamidreza Hosseinzadeh, and Zahra Einalou, "Dynamics of frontal cortex functional connectivity during cognitive tasks: insights from fNIRS analysis in the Dual n-back Paradigm." Cognitive Processing, pp. 1-12, (2025).
34- Sima Shirzadi, Zahra Einalou, and Mehrdad Dadgostar, "Investigation of functional connectivity during working memory task and hemispheric lateralization in left-and right-handers measured by fNIRS." Optik, Vol. 221p. 165347, (2020).
35- M Dadgostar, SK Setarehdan, and A Akin, "Detection of motion artifacts in fNIRS via the continuous wavelet transform." in 2013 20th Iranian Conference on Biomedical Engineering (ICBME), (2013): IEEE, pp. 243-46.
36- Behnam Molavi and Guy A Dumont, "Wavelet-based motion artifact removal for functional near-infrared spectroscopy." Physiological measurement, Vol. 33 (No. 2), p. 259, (2012).
37- Gihyoun Lee, Seung Hyun Lee, Hyeon Jin Sang, and Jinung An, "Baseline drift detection index using wavelet transform analysis for fNIRS signal." in 2017 5th International Winter Conference on Brain-Computer Interface (BCI), (2017): IEEE, pp. 73-76.
38- Ata Akin, Sima Shirzadi, Mehrdad Dadgostar, Zahra Einalou, and Sinem Burcu Erdogan, "Sex Based Differences in Functional Connectivity during a Working Memory Task: an fNIRS Study." Frontiers in Psychology, Vol. 15p. 1207202.
39- Omid Asadi, Mahsan Hajihosseini, Sima Shirzadi, Zahra Einalou, and Mehrdad Dadgostar, "Improvement of classification accuracy of functional near-infrared spectroscopy signals for hand motion and motor imagery using a common spatial pattern algorithm." Intelligent Medicine, Vol. 5 (No. 02), pp. 123-31, (2025).
40- Baolei Xu, Yunfa Fu, Lei Miao, Zhidong Wang, and Hongyi Li, "Classification of fNIRS data using wavelets and support vector machine during speed and force imagination." in 2011 IEEE International Conference on Robotics and Biomimetics, (2011): IEEE, pp. 1224-29.
41- Amanda Yumi Ambriola Oku, Candida Barreto, Guilherme Bruneri, Guilherme Brockington, Andre Fujita, and João Ricardo Sato, "Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms." Frontiers in computational neuroscience, Vol. 16p. 975743, (2022).
42- Mehrdad Dadgostar, Seyed Kamaledin Setarehdan, Sohrab Shahzadi, and Ata Akin, "Classification of schizophrenia using SVM via fNIRS." Biomedical Engineering: Applications, Basis and Communications, Vol. 30 (No. 02), p. 1850008, (2018).
43- Yun Kwan Kim, Eunhee Park, Ahee Lee, Chang-Hwan Im, and Yun-Hee Kim, "Changes in network connectivity during motor imagery and execution." PLoS One, Vol. 13 (No. 1), p. e0190715, (2018).
44- Shi-Yang Xu et al., "Altered functional connectivity in the motor and prefrontal cortex for children with Down's syndrome: an fNIRS study." Frontiers in Human Neuroscience, Vol. 14p. 6, (2020).
45- Chao Chen et al., "G-causality brain connectivity differences of finger movements between motor execution and motor imagery." Journal of healthcare engineering, Vol. 2019(2019).
2- Shirley M Coyle, Tomás E Ward, and Charles M Markham, "Brain–computer interface using a simplified functional near-infrared spectroscopy system." Journal of neural engineering, Vol. 4 (No. 3), p. 219, (2007).
3- Shirley Coyle, Tomás Ward, Charles Markham, and Gary McDarby, "On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces." Physiological measurement, Vol. 25 (No. 4), p. 815, (2004).
4- Vera Kaiser et al., "Cortical effects of user training in a motor imagery based brain–computer interface measured by fNIRS and EEG." Neuroimage, Vol. 85pp. 432-44, (2014).
5- Sarah D Power, Tiago H Falk, and Tom Chau, "Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy." Journal of neural engineering, Vol. 7 (No. 2), p. 026002, (2010).
6- Sarah D Power, Azadeh Kushki, and Tom Chau, "Towards a system-paced near-infrared spectroscopy brain–computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state." Journal of neural engineering, Vol. 8 (No. 6), p. 066004, (2011).
7- Tiago H Falk, Mirna Guirgis, Sarah Power, and Tom T Chau, "Taking NIRS-BCIs outside the lab: towards achieving robustness against environment noise." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 19 (No. 2), pp. 136-46, (2010).
8- Masayoshi Naito, Yohko Michioka, Kuniaki Ozawa, Yoshitoshi Ito, Masashi Kiguchi, and Tsuneo Kanazawa, "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, Vol. 90 (No. 7), pp. 1028-37, (2007).
9- Berdakh Abibullaev and Jinung An, "Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms." Medical engineering & physics, Vol. 34 (No. 10), pp. 1394-410, (2012).
10- Mohammadreza Abtahi, Amir Mohammad Amiri, Dennis Byrd, and Kunal Mankodiya, "Hand motion detection in fNIRS neuroimaging data." in Healthcare, (2017), Vol. 5 (No. 2): MDPI, p. 20.
11- Hendrik Santosa and Keum-Shik Hong, "An fNIRS-based BCI for mental arithmetic task using ICA." in 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems, (2013): IEEE, pp. 219-23.
12- Günther Bauernfeind, David Steyrl, Clemens Brunner, and Gernot R Müller-Putz, "Single trial classification of fNIRS-based brain-computer interface mental arithmetic data: a comparison between different classifiers." in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (2014): IEEE, pp. 2004-07.
13- Gert Pfurtscheller et al., "The hybrid BCI." Frontiers in neuroscience, Vol. 4p. 3, (2010).
14- Jaeyoung Shin, Klaus-R Müller, and Han-Jeong Hwang, "Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic." Scientific reports, Vol. 6 (No. 1), pp. 1-11, (2016).
15- Larissa C Schudlo and Tom Chau, "Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest." Journal of neural engineering, Vol. 11 (No. 1), p. 016003, (2013).
16- Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, and Keum-Shik Hong, "Analysis of different classification techniques for two-class functional near-infrared spectroscopy-based brain-computer interface." Computational intelligence and neuroscience, Vol. 2016(2016).
17- Zahra Einalou, Keivan Maghooli, Seyaed Kamaledin Setarehdan, and Ata Akin, "Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS." Neurophotonics, Vol. 4 (No. 4), p. 041407, (2017).
18- Zahra Einalou, Keyvan Maghooli, Seyed-Kamaledin Setarehdan, and Ata Akin, "Functional near infrared spectroscopy to investigation of functional connectivity in schizophrenia using partial correlation." Universal Journal of Biomedical Engineering, Vol. 2 (No. 1), pp. 5-8, (2014).
19- Liwei Xu, Bitian Wang, Gongcheng Xu, Wei Wang, Zhian Liu, and Zengyong Li, "Functional connectivity analysis using fNIRS in healthy subjects during prolonged simulated driving." Neuroscience letters, Vol. 640pp. 21-28, (2017).
20- Takashi Hanakawa, Michael A Dimyan, and Mark Hallett, "Motor planning, imagery, and execution in the distributed motor network: a time-course study with functional MRI." Cerebral cortex, Vol. 18 (No. 12), pp. 2775-88, (2008).
21- Jörn Munzert, Britta Lorey, and Karen Zentgraf, "Cognitive motor processes: the role of motor imagery in the study of motor representations." Brain research reviews, Vol. 60 (No. 2), pp. 306-26, (2009).
22- Nikhil Sharma, Peter S Jones, TA Carpenter, and Jean-Claude Baron, "Mapping the involvement of BA 4a and 4p during motor imagery." Neuroimage, Vol. 41 (No. 1), pp. 92-99, (2008).
23- Ana Solodkin, Petr Hlustik, E Elinor Chen, and Steven L Small, "Fine modulation in network activation during motor execution and motor imagery." Cerebral cortex, Vol. 14 (No. 11), pp. 1246-55, (2004).
24- Jinung An et al., "Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: An fNIRS pilot study." in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2013): IEEE, pp. 6345-48.
25- Johann P Kuhtz‐Buschbeck, C Mahnkopf, C Holzknecht, H Siebner, S Ulmer, and O Jansen, "Effector‐independent representations of simple and complex imagined finger movements: A combined fMRI and TMS study." European Journal of Neuroscience, Vol. 18 (No. 12), pp. 3375-87, (2003).
26- Christoph Stippich, Henrik Ochmann, and Klaus Sartor, "Somatotopic mapping of the human primary sensorimotor cortex during motor imagery and motor execution by functional magnetic resonance imaging." Neuroscience letters, Vol. 331 (No. 1), pp. 50-54, (2002).
27- Carlo A Porro et al., "Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study." Journal of Neuroscience, Vol. 16 (No. 23), pp. 7688-98, (1996).
28- Rui Zhang, Yamin Yan, Yuxia Hu, Li Shi, and Hong Wan, "EEG function network analysis of left and right hand motor imagery." in 2017 Chinese Automation Congress (CAC), (2017): IEEE, pp. 346-50.
29- Lulu Hu et al., "The Changes of Brain Networks Topology in Graph Theory of rt-fMRI Emotion Self-regulation Training." in International Conference on Brain Informatics, (2019): Springer, pp. 126-35.
30- Ali Dejman, Ali Khadem, and Anahita Khorrami, "Exploring the disorders of brain effective connectivity network in ASD: a case study using EEG, transfer entropy, and graph theory." in 2017 Iranian Conference on Electrical Engineering (ICEE), (2017): IEEE, pp. 8-13.
31- Liang Wang et al., "Altered small‐world brain functional networks in children with attention‐deficit/hyperactivity disorder." Human brain mapping, Vol. 30 (No. 2), pp. 638-49, (2009).
32- Jessica Guilbert, François Jouen, and Michèle Molina, "Motor imagery development and proprioceptive integration: Which sensory reweighting during childhood?" Journal of experimental child psychology, Vol. 166pp. 621-34, (2018).
33- Sima Shirzadi, Mehrdad Dadgostar, Hamidreza Hosseinzadeh, and Zahra Einalou, "Dynamics of frontal cortex functional connectivity during cognitive tasks: insights from fNIRS analysis in the Dual n-back Paradigm." Cognitive Processing, pp. 1-12, (2025).
34- Sima Shirzadi, Zahra Einalou, and Mehrdad Dadgostar, "Investigation of functional connectivity during working memory task and hemispheric lateralization in left-and right-handers measured by fNIRS." Optik, Vol. 221p. 165347, (2020).
35- M Dadgostar, SK Setarehdan, and A Akin, "Detection of motion artifacts in fNIRS via the continuous wavelet transform." in 2013 20th Iranian Conference on Biomedical Engineering (ICBME), (2013): IEEE, pp. 243-46.
36- Behnam Molavi and Guy A Dumont, "Wavelet-based motion artifact removal for functional near-infrared spectroscopy." Physiological measurement, Vol. 33 (No. 2), p. 259, (2012).
37- Gihyoun Lee, Seung Hyun Lee, Hyeon Jin Sang, and Jinung An, "Baseline drift detection index using wavelet transform analysis for fNIRS signal." in 2017 5th International Winter Conference on Brain-Computer Interface (BCI), (2017): IEEE, pp. 73-76.
38- Ata Akin, Sima Shirzadi, Mehrdad Dadgostar, Zahra Einalou, and Sinem Burcu Erdogan, "Sex Based Differences in Functional Connectivity during a Working Memory Task: an fNIRS Study." Frontiers in Psychology, Vol. 15p. 1207202.
39- Omid Asadi, Mahsan Hajihosseini, Sima Shirzadi, Zahra Einalou, and Mehrdad Dadgostar, "Improvement of classification accuracy of functional near-infrared spectroscopy signals for hand motion and motor imagery using a common spatial pattern algorithm." Intelligent Medicine, Vol. 5 (No. 02), pp. 123-31, (2025).
40- Baolei Xu, Yunfa Fu, Lei Miao, Zhidong Wang, and Hongyi Li, "Classification of fNIRS data using wavelets and support vector machine during speed and force imagination." in 2011 IEEE International Conference on Robotics and Biomimetics, (2011): IEEE, pp. 1224-29.
41- Amanda Yumi Ambriola Oku, Candida Barreto, Guilherme Bruneri, Guilherme Brockington, Andre Fujita, and João Ricardo Sato, "Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms." Frontiers in computational neuroscience, Vol. 16p. 975743, (2022).
42- Mehrdad Dadgostar, Seyed Kamaledin Setarehdan, Sohrab Shahzadi, and Ata Akin, "Classification of schizophrenia using SVM via fNIRS." Biomedical Engineering: Applications, Basis and Communications, Vol. 30 (No. 02), p. 1850008, (2018).
43- Yun Kwan Kim, Eunhee Park, Ahee Lee, Chang-Hwan Im, and Yun-Hee Kim, "Changes in network connectivity during motor imagery and execution." PLoS One, Vol. 13 (No. 1), p. e0190715, (2018).
44- Shi-Yang Xu et al., "Altered functional connectivity in the motor and prefrontal cortex for children with Down's syndrome: an fNIRS study." Frontiers in Human Neuroscience, Vol. 14p. 6, (2020).
45- Chao Chen et al., "G-causality brain connectivity differences of finger movements between motor execution and motor imagery." Journal of healthcare engineering, Vol. 2019(2019).
| Files | ||
| Issue | Vol 13 No 2 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i2.21926 | |
| Keywords | ||
| Functional Near-Infrared Spectroscopy Motor Imagery Graph Theory Small World Network Functional Connectivity | ||
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How to Cite
1.
Hajihosseini M, Asadi O, Shirzadi S, Einalou Z, Dadgostar M. Exploring Brain Functional Connectivity in Hand Motion and Motor Imagery through fNIRS Signals: A Graph Theory Approach. Frontiers Biomed Technol. 2026;13(2):294-305.
