Estimating the Relationship Between EMG Signals and EEG Signal Connections Using Convolutional Neural Networks
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Abstract
Purpose: Understanding the functional relationships between different parts of the human body can enhance the control of Brain-Computer Interface (BCI) systems. The brain, as the decision-making organ, controls all body parts to perform activities. In this study, the main objective is to estimate the activation of hand muscles and the effect of each muscle on another using Electroencephalogram (EEG) signals.
Materials and Methods: To discover the connection of hand muscles through brain signals, brain connections are extracted as influential components, and a convolutional network is utilized to assess the impact of EEG signals on the relationships among hand muscles. Five different connectivity methods were used to analyze the connections between EEG signal channels, such as correlation, coherence, the directed transfer function, Granger causality, and the phase delay index. The relationships between electromyogram (EMG) signal channels are also calculated using Granger causality. Signals are recorded in two phases: rest and activity, and ultimately, the EMG signal activity is estimated solely using EEG signals.
Results: Simulation results estimate the correlation between the estimated and actual patterns for test data to be around 0.949, indicating a high correlation between the estimated outputs and actual values.
Conclusion: Research indicates that exploring techniques for calculating relationships can be useful in evaluating the synergy and causal connections between EMG and EEG signals. In comparison to alternative graph-based techniques, this approach, utilizing regression analysis, demonstrated notably superior performance. This study could contribute to advancements in rehabilitation techniques and brain-computer interfaces.
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11- Yeongdae Kim, Sorawit Stapornchaisit, Hiroyuki Kambara, Natsue Yoshimura, and Yasuharu Koike, "Muscle Synergy and Musculoskeletal Model‐Based Continuous Multi‐Dimensional Estimation of Wrist and Hand Motions." Journal of Healthcare Engineering, Vol. 2020 (No. 1), p. 5451219, (2020).
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21- Ana Lía Albarracín, Fernando Daniel Farfán, Leonardo Ariel Cano, Juan D Romero-Ante, Juan S Montenegro-Bravo, and Eduardo Fernandez-Jover, "Muscle Networks Dynamic in Demanding Postural Tasks and Visual Feedback Privation: A Preliminary Study." Engineering Proceedings, Vol. 81 (No. 1), p. 6, (2025).
22- Jacob G Tryon, "Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation." The University of Western Ontario (Canada), (2023).
23- Mohamed Abdelhady, Diane L Damiano, and Thomas C Bulea, "Knee angle estimation from surface EMG during walking using attention-based deep recurrent neural networks: Feasibility and initial demonstration in cerebral palsy." Sensors, Vol. 24 (No. 13), p. 4217, (2024).
24- Dario Farina, Roberto Merletti, and Roger M Enoka, "The extraction of neural strategies from the surface EMG." Journal of applied physiology, Vol. 96 (No. 4), pp. 1486-95, (2004).
25- Nozomu Hoshimiya, Akira Naito, Michihiro Yajima, and Yasunobu Handa, "A multichannel FES system for the restoration of motor functions in high spinal cord injury patients: a respiration-controlled system for multijoint upper extremity." IEEE Transactions on Biomedical Engineering, Vol. 36 (No. 7), pp. 754-60, (2002).
26- Katarzyna J Blinowska, "Review of the methods of determination of directed connectivity from multichannel data." Medical & biological engineering & computing, Vol. 49pp. 521-29, (2011).
27- Alaa Tharwat, "Independent component analysis: An introduction." Applied Computing and Informatics, Vol. 17 (No. 2), pp. 222-49, (2021).
28- Alex Fornito, Andrew Zalesky, and Edward Bullmore, Fundamentals of brain network analysis. Academic press, (2016).
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30- Supat Saetia, Natsue Yoshimura, and Yasuharu Koike, "Constructing brain connectivity model using causal network reconstruction approach." Frontiers in Neuroinformatics, Vol. 15p. 619557, (2021).
31- Christopher J Quinn, Todd P Coleman, Negar Kiyavash, and Nicholas G Hatsopoulos, "Estimating the directed information to infer causal relationships in ensemble neural spike train recordings." Journal of computational neuroscience, Vol. 30pp. 17-44, (2011).
32- John Geweke, "Measurement of linear dependence and feedback between multiple time series." Journal of the American statistical association, Vol. 77 (No. 378), pp. 304-13, (1982).
33- J Zygierewicz, "Multiple channels (multivariate) signals." Practical Biomedical Signal Analysis Using MATLAB®. Series: Series in Medical Physics and Biomedical Engineering, pp. 75-100, (2011).
34- J Ginter Jr, KJ Blinowska, M Kamiński, PJ Durka, Gert Pfurtscheller, and Christa Neuper, "Propagation of EEG activity in the beta and gamma band during movement imagery in humans." Methods of information in medicine, Vol. 44 (No. 01), pp. 106-13, (2005).
35- Joseph Lee Rodgers and W Alan Nicewander, "Thirteen ways to look at the correlation coefficient." The American Statistician, Vol. 42 (No. 1), pp. 59-66, (1988).
36- Marat Kozhikov, Paata Janelidze, Akbilek Seitmukhanbet, Yessekina Aiman, Almas Karymsakov, and Timoth Mkilima, "Community-centric carbon reduction initiatives and their impact on grid emission factors: A case study in Kazakhstan." (2024).
37- Katarzyna J Blinowska and Maciej Kamiński, "Multivariate signal analysis by parametric models." Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, pp. 373-409, (2006).
38- Marcin Jan Kaminski and Katarzyna J Blinowska, "A new method of the description of the information flow in the brain structures." Biological cybernetics, Vol. 65 (No. 3), pp. 203-10, (1991).
39- Maciej Kamiński, Mingzhou Ding, Wilson A Truccolo, and Steven L Bressler, "Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance." Biological cybernetics, Vol. 85pp. 145-57, (2001).
40- Anna Korzeniewska, Małgorzata Mańczak, Maciej Kamiński, Katarzyna J Blinowska, and Stefan Kasicki, "Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method." Journal of neuroscience methods, Vol. 125 (No. 1-2), pp. 195-207, (2003).
41- Emery N Brown, Robert E Kass, and Partha P Mitra, "Multiple neural spike train data analysis: state-of-the-art and future challenges." Nature neuroscience, Vol. 7 (No. 5), pp. 456-61, (2004).
42- Bernat Kocsis and Maciej Kaminski, "Dynamic changes in the direction of the theta rhythmic drive between supramammillary nucleus and the septohippocampal system." Hippocampus, Vol. 16 (No. 6), pp. 531-40, (2006).
43- Amir Hossein Ghaderi, Shadi Moradkhani, Arvin Haghighatfard, Fatemeh Akrami, Zahra Khayyer, and Fuat Balcı, "Time estimation and beta segregation: An EEG study and graph theoretical approach." PLoS One, Vol. 13 (No. 4), p. e0195380, (2018).
44- Olmo Durán Medina, François G Schmitt, Rudy Calif, Grégory Germain, and Benoît Gaurier, "Turbulence analysis and multiscale correlations between synchronized flow velocity and marine turbine power production." Renewable Energy, Vol. 112pp. 314-27, (2017).
45- Shi Dong, Ping Wang, and Khushnood Abbas, "A survey on deep learning and its applications." Computer Science Review, Vol. 40p. 100379, (2021).
46- Patricia Pauli, Dennis Gramlich, and Frank Allgöwer, "State space representations of the Roesser type for convolutional layers." IFAC-PapersOnLine, Vol. 58 (No. 17), pp. 344-49, (2024).
47- P Nirmaladevi, M Pavithra, H Balasubramani, and V Manoj, "Mobile application based sign language system for hearing and speech impaired people." in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), (2023): IEEE, pp. 1-7.
48- Diederik P Kingma, "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980, (2014).
49- Imran Khan Mohd Jais, Amelia Ritahani Ismail, and Syed Qamrun Nisa, "Adam optimization algorithm for wide and deep neural network." Knowl. Eng. Data Sci., Vol. 2 (No. 1), pp. 41-46, (2019).
50- Morteza Ziari, Hojat Karami, Ali Ostadi, and Hamidreza Ghazvinian, "Simulation and prediction of hydraulic jump characteristics over expanding rough beds using FLOW-3D and soft computing techniques." Journal of Hydroinformatics, Vol. 27 (No. 1), pp. 88-106, (2024).
2- Hessam Ahmadi, Emad Fatemizadeh, and Ali Motie-Nasrabadi, "Multiclass classification of patients during different stages of Alzheimer’s disease using fMRI time-series." Biomedical Physics & Engineering Express, Vol. 6 (No. 5), p. 055022, (2020).
3- Elham Samadi, Hessam Ahmadi, and Fereidoun Nowshiravan Rahatabad, "Analysis of hand tremor in parkinson’s disease: Frequency domain approach." Frontiers in Biomedical Technologies, Vol. 7 (No. 2), pp. 105-11, (2020).
4- Hessam Ahmadi, Emad Fatemizadeh, and Ali Motie-Nasrabadi, "fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease." Signal, Image and Video Processing, Vol. 15pp. 715-23, (2021).
5- Ali Ekhlasi, Hessam Ahmadi, Amir Molavi, Mohammad Saadat Nia, and Ali Motie Nasrabadi, "EEG signal analysis during Ishihara’s test in subjects with normal vision and color vision deficiency." Biomedical Physics & Engineering Express, Vol. 7 (No. 2), p. 025008, (2021).
6- Jerry J Shih, Dean J Krusienski, and Jonathan R Wolpaw, "Brain-computer interfaces in medicine." in Mayo clinic proceedings, (2012), Vol. 87 (No. 3): Elsevier, pp. 268-79.
7- Ferruccio Panzica, Laura Canafoglia, and Silvana Franceschetti, "EEG–EMG information flow in movement-activated myoclonus in patients with Unverricht–Lundborg disease." Clinical Neurophysiology, Vol. 125 (No. 9), pp. 1803-08, (2014).
8- Luis Velázquez-Pérez et al., "Early corticospinal tract damage in prodromal SCA2 revealed by EEG-EMG and EMG-EMG coherence." Clinical Neurophysiology, Vol. 128 (No. 12), pp. 2493-502, (2017).
9- Eduardo Loopez-Larraz, Niels Birbaumer, and Ander Ramos-Murguialday, "A hybrid EEG-EMG BMI improves the detection of movement intention in cortical stroke patients with complete hand paralysis." in 2018 40th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2018): IEEE, pp. 2000-03.
10- Mahdi Jalili, "Graph theoretical analysis of Alzheimer's disease: Discrimination of AD patients from healthy subjects." Information Sciences, Vol. 384pp. 145-56, (2017).
11- Yeongdae Kim, Sorawit Stapornchaisit, Hiroyuki Kambara, Natsue Yoshimura, and Yasuharu Koike, "Muscle Synergy and Musculoskeletal Model‐Based Continuous Multi‐Dimensional Estimation of Wrist and Hand Motions." Journal of Healthcare Engineering, Vol. 2020 (No. 1), p. 5451219, (2020).
12- Daniele Borzelli, Sergio Gurgone, Paolo De Pasquale, Nicola Lotti, Andrea d’Avella, and Laura Gastaldi, "Use of surface electromyography to estimate end-point force in redundant systems: comparison between linear approaches." Bioengineering, Vol. 10 (No. 2), p. 234, (2023).
13- Nicole Donahue, Developing Muscle Synergy Functions for Remote Gait Analysis. The University of Vermont and State Agricultural College, (2023).
14- Tanaya Das, Lakhyajit Gohain, Nayan M Kakoty, MB Malarvili, Prihartini Widiyanti, and Gajendra Kumar, "Hierarchical approach for fusion of electroencephalography and electromyography for predicting finger movements and kinematics using deep learning." Neurocomputing, Vol. 527pp. 184-95, (2023).
15- Chenyang Li et al., "Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury." Frontiers in Neuroscience, Vol. 19p. 1532099, (2025).
16- Daniel Wang et al., "Hybrid Brain-Machine Interface: Integrating EEG and EMG for Reduced Physical Demand." arXiv preprint arXiv:2502.10904, (2025).
17- Gordon Alderink, Diana McCrumb, David Zeitler, and Samhita Rhodes, "Analysis of Connectivity in Electromyography Signals to Examine Neural Correlations in the Activation of Lower Leg Muscles for Postural Stability: A Pilot Study." Bioengineering, Vol. 12 (No. 1), p. 84, (2025).
18- Paulo Cezar Rocha Dos Santos, Claudine JC Lamoth, Fabio Augusto Barbieri, Inge Zijdewind, Lilian Teresa Bucken Gobbi, and Tibor Hortobágyi, "Age-specific modulation of intermuscular beta coherence during gait before and after experimentally induced fatigue." Scientific reports, Vol. 10 (No. 1), p. 15854, (2020).
19- Costanza Armanini, Tuka Alhanai, Farah E Shamout, and S Farokh Atashzar, "The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces." arXiv preprint arXiv:2408.02547, (2024).
20- Isabel Junquera-Godoy et al., "Muscle network connectivity study in diabetic peripheral neuropathy patients." Sensors, Vol. 24 (No. 15), p. 4954, (2024).
21- Ana Lía Albarracín, Fernando Daniel Farfán, Leonardo Ariel Cano, Juan D Romero-Ante, Juan S Montenegro-Bravo, and Eduardo Fernandez-Jover, "Muscle Networks Dynamic in Demanding Postural Tasks and Visual Feedback Privation: A Preliminary Study." Engineering Proceedings, Vol. 81 (No. 1), p. 6, (2025).
22- Jacob G Tryon, "Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation." The University of Western Ontario (Canada), (2023).
23- Mohamed Abdelhady, Diane L Damiano, and Thomas C Bulea, "Knee angle estimation from surface EMG during walking using attention-based deep recurrent neural networks: Feasibility and initial demonstration in cerebral palsy." Sensors, Vol. 24 (No. 13), p. 4217, (2024).
24- Dario Farina, Roberto Merletti, and Roger M Enoka, "The extraction of neural strategies from the surface EMG." Journal of applied physiology, Vol. 96 (No. 4), pp. 1486-95, (2004).
25- Nozomu Hoshimiya, Akira Naito, Michihiro Yajima, and Yasunobu Handa, "A multichannel FES system for the restoration of motor functions in high spinal cord injury patients: a respiration-controlled system for multijoint upper extremity." IEEE Transactions on Biomedical Engineering, Vol. 36 (No. 7), pp. 754-60, (2002).
26- Katarzyna J Blinowska, "Review of the methods of determination of directed connectivity from multichannel data." Medical & biological engineering & computing, Vol. 49pp. 521-29, (2011).
27- Alaa Tharwat, "Independent component analysis: An introduction." Applied Computing and Informatics, Vol. 17 (No. 2), pp. 222-49, (2021).
28- Alex Fornito, Andrew Zalesky, and Edward Bullmore, Fundamentals of brain network analysis. Academic press, (2016).
29- CJ Stam et al., "The relation between structural and functional connectivity patterns in complex brain networks." International Journal of Psychophysiology, Vol. 103pp. 149-60, (2016).
30- Supat Saetia, Natsue Yoshimura, and Yasuharu Koike, "Constructing brain connectivity model using causal network reconstruction approach." Frontiers in Neuroinformatics, Vol. 15p. 619557, (2021).
31- Christopher J Quinn, Todd P Coleman, Negar Kiyavash, and Nicholas G Hatsopoulos, "Estimating the directed information to infer causal relationships in ensemble neural spike train recordings." Journal of computational neuroscience, Vol. 30pp. 17-44, (2011).
32- John Geweke, "Measurement of linear dependence and feedback between multiple time series." Journal of the American statistical association, Vol. 77 (No. 378), pp. 304-13, (1982).
33- J Zygierewicz, "Multiple channels (multivariate) signals." Practical Biomedical Signal Analysis Using MATLAB®. Series: Series in Medical Physics and Biomedical Engineering, pp. 75-100, (2011).
34- J Ginter Jr, KJ Blinowska, M Kamiński, PJ Durka, Gert Pfurtscheller, and Christa Neuper, "Propagation of EEG activity in the beta and gamma band during movement imagery in humans." Methods of information in medicine, Vol. 44 (No. 01), pp. 106-13, (2005).
35- Joseph Lee Rodgers and W Alan Nicewander, "Thirteen ways to look at the correlation coefficient." The American Statistician, Vol. 42 (No. 1), pp. 59-66, (1988).
36- Marat Kozhikov, Paata Janelidze, Akbilek Seitmukhanbet, Yessekina Aiman, Almas Karymsakov, and Timoth Mkilima, "Community-centric carbon reduction initiatives and their impact on grid emission factors: A case study in Kazakhstan." (2024).
37- Katarzyna J Blinowska and Maciej Kamiński, "Multivariate signal analysis by parametric models." Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, pp. 373-409, (2006).
38- Marcin Jan Kaminski and Katarzyna J Blinowska, "A new method of the description of the information flow in the brain structures." Biological cybernetics, Vol. 65 (No. 3), pp. 203-10, (1991).
39- Maciej Kamiński, Mingzhou Ding, Wilson A Truccolo, and Steven L Bressler, "Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance." Biological cybernetics, Vol. 85pp. 145-57, (2001).
40- Anna Korzeniewska, Małgorzata Mańczak, Maciej Kamiński, Katarzyna J Blinowska, and Stefan Kasicki, "Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method." Journal of neuroscience methods, Vol. 125 (No. 1-2), pp. 195-207, (2003).
41- Emery N Brown, Robert E Kass, and Partha P Mitra, "Multiple neural spike train data analysis: state-of-the-art and future challenges." Nature neuroscience, Vol. 7 (No. 5), pp. 456-61, (2004).
42- Bernat Kocsis and Maciej Kaminski, "Dynamic changes in the direction of the theta rhythmic drive between supramammillary nucleus and the septohippocampal system." Hippocampus, Vol. 16 (No. 6), pp. 531-40, (2006).
43- Amir Hossein Ghaderi, Shadi Moradkhani, Arvin Haghighatfard, Fatemeh Akrami, Zahra Khayyer, and Fuat Balcı, "Time estimation and beta segregation: An EEG study and graph theoretical approach." PLoS One, Vol. 13 (No. 4), p. e0195380, (2018).
44- Olmo Durán Medina, François G Schmitt, Rudy Calif, Grégory Germain, and Benoît Gaurier, "Turbulence analysis and multiscale correlations between synchronized flow velocity and marine turbine power production." Renewable Energy, Vol. 112pp. 314-27, (2017).
45- Shi Dong, Ping Wang, and Khushnood Abbas, "A survey on deep learning and its applications." Computer Science Review, Vol. 40p. 100379, (2021).
46- Patricia Pauli, Dennis Gramlich, and Frank Allgöwer, "State space representations of the Roesser type for convolutional layers." IFAC-PapersOnLine, Vol. 58 (No. 17), pp. 344-49, (2024).
47- P Nirmaladevi, M Pavithra, H Balasubramani, and V Manoj, "Mobile application based sign language system for hearing and speech impaired people." in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), (2023): IEEE, pp. 1-7.
48- Diederik P Kingma, "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980, (2014).
49- Imran Khan Mohd Jais, Amelia Ritahani Ismail, and Syed Qamrun Nisa, "Adam optimization algorithm for wide and deep neural network." Knowl. Eng. Data Sci., Vol. 2 (No. 1), pp. 41-46, (2019).
50- Morteza Ziari, Hojat Karami, Ali Ostadi, and Hamidreza Ghazvinian, "Simulation and prediction of hydraulic jump characteristics over expanding rough beds using FLOW-3D and soft computing techniques." Journal of Hydroinformatics, Vol. 27 (No. 1), pp. 88-106, (2024).
| Files | ||
| Issue | Vol 13 No 1 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i1.20759 | |
| Keywords | ||
| Vital Signal Connections Brain Computer Interface Regression Convolutional Networks | ||
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How to Cite
1.
Samadi E, Nowshiaravan Rahatabad F, Motie nasrabadi A, Jafarnia Dabanloo N. Estimating the Relationship Between EMG Signals and EEG Signal Connections Using Convolutional Neural Networks. Frontiers Biomed Technol. 2026;13(1):40-53.
