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Panda R, Vanhaudenhuyse A, Piarulli A, Annen J, Demertzi A, Alnagger N, Chennu S, Laureys S, Faymonville ME, Gosseries O. Altered Brain Connectivity and Network Topological Organization in a Non-ordinary State of Consciousness Induced by Hypnosis. J Cogn Neurosci 2023; 35:1394-1409. [PMID: 37315333 DOI: 10.1162/jocn_a_02019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Hypnosis has been shown to be of clinical utility; however, its underlying neural mechanisms remain unclear. This study aims to investigate altered brain dynamics during the non-ordinary state of consciousness induced by hypnosis. We studied high-density EEG in 9 healthy participants during eyes-closed wakefulness and during hypnosis, induced by a muscle relaxation and eyes fixation procedure. Using hypotheses based on internal and external awareness brain networks, we assessed region-wise brain connectivity between six ROIs (right and left frontal, right and left parietal, upper and lower midline regions) at the scalp level and compared across conditions. Data-driven, graph-theory analyses were also carried out to characterize brain network topology in terms of brain network segregation and integration. During hypnosis, we observed (1) increased delta connectivity between left and right frontal, as well as between right frontal and parietal regions; (2) decreased connectivity for alpha (between right frontal and parietal and between upper and lower midline regions) and beta-2 bands (between upper midline and right frontal, frontal and parietal, also between upper and lower midline regions); and (3) increased network segregation (short-range connections) in delta and alpha bands, and increased integration (long-range connections) in beta-2 band. This higher network integration and segregation was measured bilaterally in frontal and right parietal electrodes, which were identified as central hub regions during hypnosis. This modified connectivity and increased network integration-segregation properties suggest a modification of the internal and external awareness brain networks that may reflect efficient cognitive-processing and lower incidences of mind-wandering during hypnosis.
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Affiliation(s)
| | | | | | - Jitka Annen
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Naji Alnagger
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Steven Laureys
- University of Liège, Belgium
- University Hospital of Liège, Belgium
- Laval University, Québec, Canada
| | | | - Olivia Gosseries
- University of Liège, Belgium
- University Hospital of Liège, Belgium
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Chen S, Shu X, Wang H, Ding L, Fu J, Jia J. The Differences Between Motor Attempt and Motor Imagery in Brain-Computer Interface Accuracy and Event-Related Desynchronization of Patients With Hemiplegia. Front Neurorobot 2021; 15:706630. [PMID: 34803647 PMCID: PMC8602190 DOI: 10.3389/fnbot.2021.706630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Motor attempt and motor imagery (MI) are two common motor tasks used in brain-computer interface (BCI). They are widely researched for motor rehabilitation in patients with hemiplegia. The differences between the motor attempt (MA) and MI tasks of patients with hemiplegia can be used to promote BCI application. This study aimed to explore the accuracy of BCI and event-related desynchronization (ERD) between the two tasks. Materials and Methods: We recruited 13 patients with stroke and 3 patients with traumatic brain injury, to perform MA and MI tasks in a self-control design. The BCI accuracies from the bilateral, ipsilesional, and contralesional hemispheres were analyzed and compared between different tasks. The cortical activation patterns were evaluated with ERD and laterality index (LI). Results: The study showed that the BCI accuracies of MA were significantly (p < 0.05) higher than MI in the bilateral, ipsilesional, and contralesional hemispheres in the alpha-beta (8–30 Hz) frequency bands. There was no significant difference in ERD and LI between the MA and MI tasks in the 8–30 Hz frequency bands. However, in the MA task, there was a negative correlation between the ERD values in the channel CP1 and ipsilesional hemispheric BCI accuracies (r = −0.552, p = 0.041, n = 14) and a negative correlation between the ERD values in channel CP2 and bilateral hemispheric BCI accuracies (r = −0.543, p = 0.045, n = 14). While in the MI task, there were negative correlations between the ERD values in channel C4 and bilateral hemispheric BCI accuracies (r = −0.582, p = 0.029, n = 14) as well as the contralesional hemispheric BCI accuracies (r = −0.657, p = 0.011, n = 14). As for motor dysfunction, there was a significant positive correlation between the ipsilesional BCI accuracies and FMA scores of the hand part in 8–13 Hz (r = 0.565, p = 0.035, n = 14) in the MA task and a significant positive correlation between the ipsilesional BCI accuracies and FMA scores of the hand part in 13–30 Hz (r = 0.558, p = 0.038, n = 14) in the MI task. Conclusion: The MA task may achieve better BCI accuracy but have similar cortical activations with the MI task. Cortical activation (ERD) may influence the BCI accuracy, which should be carefully considered in the BCI motor rehabilitation of patients with hemiplegia.
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Affiliation(s)
- Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hewei Wang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Ding
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianghong Fu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Shanghai, China
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Li H, Fan K, Ma J, Wang B, Qiao X, Yan Y, Du W, Wang L. Massage Therapy's Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2021; 9:2100320. [PMID: 33738147 PMCID: PMC7965939 DOI: 10.1109/jtehm.2021.3056911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 12/18/2020] [Accepted: 01/27/2021] [Indexed: 11/09/2022]
Abstract
Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.
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Affiliation(s)
- Huihui Li
- Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China
| | - Kai Fan
- North China Institute of Aerospace EngineeringLangfang065000China
| | - Junsong Ma
- School of Electronic Engineering and AutomationGuilin University of Electronic TechnologyGuilin541004China
| | - Bo Wang
- Electronic and Communication Engineering DepartmentWuhan University of TechnologyWuhan430070China
| | - Xiaohao Qiao
- Electronic and Communication Engineering DepartmentWuhan University of TechnologyWuhan430070China
| | - Yan Yan
- Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China
| | - Wenjing Du
- Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China
| | - Lei Wang
- Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China
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Jiang X, Lopez E, Stieger JR, Greco CM, He B. Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning. Front Neurosci 2021; 14:584971. [PMID: 33551719 PMCID: PMC7858648 DOI: 10.3389/fnins.2020.584971] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/21/2020] [Indexed: 12/20/2022] Open
Abstract
Sensorimotor rhythm (SMR)-based brain-computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery. Despite the non-invasiveness, ease of use, and low cost, this kind of BCI has limitations due to long training times and BCI inefficiency-that is, the SMR BCI control paradigm may not work well on a subpopulation of users. Meditation is a mental training method to improve mindfulness and awareness and is reported to have positive effects on one's mental state. Here, we investigated the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in one-dimensional (1D) and two-dimensional (2D) cursor control tasks. We found numerical evidence that meditators outperformed control subjects in both tasks (1D and 2D), and there were fewer BCI inefficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups and showed that the meditators had a higher resting SMR predictor, more stable resting mu rhythm, and a larger control signal contrast than controls during the task.
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Affiliation(s)
- Xiyuan Jiang
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Emily Lopez
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - James R. Stieger
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Carol M. Greco
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
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Lindig-León C, Rimbert S, Bougrain L. Multiclass Classification Based on Combined Motor Imageries. Front Neurosci 2020; 14:559858. [PMID: 33328845 PMCID: PMC7710761 DOI: 10.3389/fnins.2020.559858] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/13/2020] [Indexed: 11/13/2022] Open
Abstract
Motor imagery (MI) allows the design of self-paced brain–computer interfaces (BCIs), which can potentially afford an intuitive and continuous interaction. However, the implementation of non-invasive MI-based BCIs with more than three commands is still a difficult task. First, the number of MIs for decoding different actions is limited by the constraint of maintaining an adequate spacing among the corresponding sources, since the electroencephalography (EEG) activity from near regions may add up. Second, EEG generates a rather noisy image of brain activity, which results in a poor classification performance. Here, we propose a solution to address the limitation of identifiable motor activities by using combined MIs (i.e., MIs involving 2 or more body parts at the same time). And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. We recorded EEG signals from seven healthy subjects during an 8-class EEG experiment including the rest condition and all possible combinations using the left hand, right hand, and feet. The proposed multilabel approaches convert the original 8-class problem into a set of three binary problems to facilitate the use of the CSP algorithm. In the case of the MC2CMI method, each binary problem groups together in one class all the MIs engaging one of the three selected body parts, while the rest of MIs that do not engage the same body part are grouped together in the second class. In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. Finally, three sets of features are merged together to predict the user intention by applying an 8-class linear discriminant analysis. The MC2SMI method is quite similar, the only difference is that any of the combined MIs is considered during the training phase, which drastically accelerates the calibration time. For all subjects, both the MC2CMI and the MC2SMI approaches reached a higher accuracy than the classic pair-wise (PW) and one-vs.-all (OVA) methods. Our results show that, when brain activity is properly modulated, multilabel approaches represent a very interesting solution to increase the number of commands, and thus to provide a better interaction.
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Affiliation(s)
- Cecilia Lindig-León
- Université de Lorraine, CNRS, LORIA, Inria, Nancy, France.,Faculty of Engineering, Computer Science and Psychology, Institute of Neural Information Processing, Ulm University, Ulm, Germany
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Lotte F, Jeunet C, Chavarriaga R, Bougrain L, Thompson DE, Scherer R, Mowla MR, Kübler A, Grosse-Wentrup M, Dijkstra K, Dayan N. Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research. BRAIN-COMPUTER INTERFACES 2020. [DOI: 10.1080/2326263x.2019.1697143] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Fabien Lotte
- Inria, LaBRI, CNRS/University of Bordeaux/Bordeaux INP, Bordeaux, France
| | - Camille Jeunet
- CLLE Lab, CNRS, University of Toulouse Jean Jaurès, Toulouse, France
| | - Ricardo Chavarriaga
- Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | | | - Dave E. Thompson
- Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Reinhold Scherer
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Md Rakibul Mowla
- Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Wurzburg, Germany
| | - Moritz Grosse-Wentrup
- Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Karen Dijkstra
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Natalie Dayan
- Intelligent Systems Research Center, Ulster University, Londonderry, Northern Ireland
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