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Rosanne O, Alves de Oliveira A, Falk TH. EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs. SENSORS (BASEL, SWITZERLAND) 2023; 23:9352. [PMID: 38067725 PMCID: PMC10708818 DOI: 10.3390/s23239352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023]
Abstract
Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.
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Affiliation(s)
- Olivier Rosanne
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
| | - Alcyr Alves de Oliveira
- Graduate Program in Psychology and Health, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil;
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
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Tang J, Xi X, Wang T, Wang J, Li L, Lü Z. Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient. J Neural Eng 2023; 20:056017. [PMID: 37683652 DOI: 10.1088/1741-2552/acf7f7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system.Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model.Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals' local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices.Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.
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Affiliation(s)
- Jianpeng Tang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Xugang Xi
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Ting Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Junhong Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Lihua Li
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, People's Republic of China
| | - Zhong Lü
- Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, People's Republic of China
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Liang T, Hong L, Xiao J, Wei L, Liu X, Wang H, Dong B, Liu X. Directed network analysis reveals changes in cortical and muscular connectivity caused by different standing balance tasks. J Neural Eng 2022; 19. [PMID: 35767971 DOI: 10.1088/1741-2552/ac7d0c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/29/2022] [Indexed: 11/12/2022]
Abstract
Objective.Standing balance forms the basis of daily activities that require the integration of multi-sensory information and coordination of multi-muscle activation. Previous studies have confirmed that the cortex is directly involved in balance control, but little is known about the neural mechanisms of cortical integration and muscle coordination in maintaining standing balance.Approach.We used a direct directed transfer function (dDTF) to analyze the changes in the cortex and muscle connections of healthy subjects (15 subjects: 13 male and 2 female) corresponding to different standing balance tasks.Main results.The results show that the topology of the EEG brain network and muscle network changes significantly as the difficulty of the balancing tasks increases. For muscle networks, the connection analysis shows that the connection of antagonistic muscle pairs plays a major role in the task. For EEG brain networks, graph theory-based analysis shows that the clustering coefficient increases significantly, and the characteristic path length decreases significantly with increasing task difficulty. We also found that cortex-to-muscle connections increased with the difficulty of the task and were significantly stronger than the muscle-to-cortex connections.Significance.These results show that changes in the difficulty of balancing tasks alter EEG brain networks and muscle networks, and an analysis based on the directed network can provide rich information for exploring the neural mechanisms of balance control.
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Affiliation(s)
- Tie Liang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Lei Hong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Jinzhuang Xiao
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Lixin Wei
- Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Xiaoguang Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Hongrui Wang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Bin Dong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Development Planning Office, Affiliated Hospital of Hebei University, Baoding 071002, People's Republic of China
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
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Keihani A, Mohammadi AM, Marzbani H, Nafissi S, Haidari MR, Jafari AH. Sparse representation of brain signals offers effective computation of cortico-muscular coupling value to predict the task-related and non-task sEMG channels: A joint hdEEG-sEMG study. PLoS One 2022; 17:e0270757. [PMID: 35776772 PMCID: PMC9249190 DOI: 10.1371/journal.pone.0270757] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 06/17/2022] [Indexed: 11/19/2022] Open
Abstract
Cortico-muscular interactions play important role in sensorimotor control during motor task and are commonly studied by cortico-muscular coherence (CMC) method using joint electroencephalogram-surface electromyogram (EEG-sEMG) signals. As noise and time delay between the two signals weaken the CMC value, coupling difference between non-task sEMG channels is often undetectable. We used sparse representation of EEG channels to compute CMC and detect coupling for task-related and non-task sEMG signals. High-density joint EEG-sEMG (53 EEG channels, 4 sEMG bipolar channels) signals were acquired from 15 subjects (30.26 ± 4.96 years) during four specific hand and foot contraction tasks (2 dynamic and 2 static contraction). Sparse representations method was applied to detect projection of EEG signals on each sEMG channel. Bayesian optimization was employed to select best-fitted method with tuned hyperparameters on the input feeding data while using 80% data as the train set and 20% as test set. K-fold (K = 5) cross-validation method was used for evaluation of trained model. Two models were trained separately, one for CMC data and the other from sparse representation of EEG channels on each sEMG channel. Sensitivity, specificity, and accuracy criteria were obtained for test dataset to evaluate the performance of task-related and non-task sEMG channels detection. Coupling values were significantly different between grand average of task-related compared to the non-task sEMG channels (Z = -6.33, p< 0.001, task-related median = 2.011, non-task median = 0.112). Strong coupling index was found even in single trial analysis. Sparse representation approach (best fitted model: SVM, Accuracy = 88.12%, Sensitivity = 83.85%, Specificity = 92.45%) outperformed CMC method (best fitted model: KNN, Accuracy = 50.83%, Sensitivity = 52.17%, Specificity = 49.47%). Sparse representation approach offers high performance to detect CMC for discerning the EMG channels involved in the contraction tasks and non-tasks.
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Affiliation(s)
- Ahmadreza Keihani
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, I.R. Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, I.R. Iran
| | - Amin Mohammad Mohammadi
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, I.R. Iran
- Department of Electrical and Computer Engineering, University of Tehran, Tehran, I.R. Iran
| | - Hengameh Marzbani
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, I.R. Iran
| | - Shahriar Nafissi
- Department of Neurology, Neuromuscular Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, I.R. Iran
| | - Mohsen Reza Haidari
- Section of Neuroscience, Department of Neurology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, I.R. Iran
- * E-mail: (AHJ); (MRH)
| | - Amir Homayoun Jafari
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, I.R. Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, I.R. Iran
- * E-mail: (AHJ); (MRH)
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Liang T, Zhang Q, Hong L, Liu X, Dong B, Wang H, Liu X. Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling. Front Neurosci 2021; 15:750936. [PMID: 34566576 PMCID: PMC8458941 DOI: 10.3389/fnins.2021.750936] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 08/20/2021] [Indexed: 12/04/2022] Open
Abstract
As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.
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Affiliation(s)
- Tie Liang
- Institute of Electric Engineering, Yanshan University, Qinhuangdao, China.,College of Electronic Information Engineering, Hebei University, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China
| | - Qingyu Zhang
- College of Electronic Information Engineering, Hebei University, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China
| | - Lei Hong
- College of Electronic Information Engineering, Hebei University, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China
| | - Xiaoguang Liu
- College of Electronic Information Engineering, Hebei University, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China
| | - Bin Dong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China.,Development Planning Office, Affiliated Hospital of Hebei University, Baoding, China
| | - Hongrui Wang
- Institute of Electric Engineering, Yanshan University, Qinhuangdao, China.,College of Electronic Information Engineering, Hebei University, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China
| | - Xiuling Liu
- College of Electronic Information Engineering, Hebei University, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China
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