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Xu X, Fan X, Dong J, Zhang X, Song Z, Li W, Pu F. Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1055-1067. [PMID: 38349835 DOI: 10.1109/tnsre.2024.3365587] [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: 02/15/2024]
Abstract
Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage. Some movements are easier to imagine from the 3PP. However, the 3PP training efficiency may be unsatisfactory, which may be attributed to the difficulty encountered when generating a strong sense of ownership (SOO). In this work, we attempt to enhance a visual-guided 3PP MI in stroke patients by eliciting the SOO over a virtual avatar with VR. We propose to achieve this by inducing the so-called out-of-body experience (OBE), which is a full-body illusion (FBI) that people misperceive a 3PP virtual body as his/her own (i.e., generating the SOO to the virtual body). Electroencephalography signals of 13 stroke patients are recorded while MI of the affected upper limb is being performed. The proposed paradigm is evaluated by comparing event-related desynchronization (ERD) with a control paradigm without FBI induction. The results show that the proposed paradigm leads to a significantly larger ERD during MI, indicating a bilateral activation pattern consistent with that in previous studies. In conclusion, 3PP MI can be enhanced in stroke patients by eliciting the SOO through induction of the "OBE" FBI. This study offers more possibilities for virtual rehabilitation in stroke patients and can further facilitate VR application in rehabilitation.
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Leng J, Zhu J, Yan Y, Yu X, Liu M, Lou Y, Liu Y, Gao L, Sun Y, He T, Yang Q, Feng C, Wang D, Zhang Y, Xu Q, Xu F. Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network - Gradient Penalty Model. Int J Neural Syst 2024; 34:2350067. [PMID: 38149912 DOI: 10.1142/s0129065723500673] [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] [Indexed: 12/28/2023]
Abstract
Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.
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Affiliation(s)
- Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Jianqun Zhu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yihao Yan
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yitai Lou
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yanbing Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Licai Gao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Yuan Sun
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Tianzheng He
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Qingbo Yang
- School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Chao Feng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
| | - Dezheng Wang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, P. R. China
| | - Yang Zhang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, P. R. China
| | - Qing Xu
- Shandong Institute of Scientific and Technical Information, Jinan 250101, P. R. China
| | - Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China
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Candia-Rivera D, Catrambone V, Barbieri R, Valenza G. A new framework for modeling the bidirectional interplay between brain oscillations and cardiac sympathovagal activity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1957-1960. [PMID: 36083927 DOI: 10.1109/embc48229.2022.9871169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The study of functional brain-heart interplay (BHI) aims to describe the dynamical interactions between central and peripheral autonomic nervous systems. Here, we introduce the Sympathovagal Synthetic Data Generation Model, which constitutes a new computational framework for the assessment of functional BHI. The model estimates the bidirectional interplay with novel quantifiers of cardiac sympathovagal activity gathered from Laguerre expansions of RR series (from the ECG), as an alternative to the classical spectral analysis. The main features of the model are time-varying coupling coefficients linking Electroencephalography (EEG) oscillations and cardiac sympathetic or parasympathetic activity, for either ascending or descending direction of the information flow. In this proof-of-concept study, functional BHI is quantified in the direction from-heart-to-brain, on data from 16 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay originating from sympathetic and parasympathetic activities and sustaining EEG oscillations mainly in the δ and γ bands. The proposed computational framework could provide a viable tool for the functional assessment of the causal interplay between cortical and cardiac sympathovagal dynamics.
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Candia-Rivera D, Catrambone V, Barbieri R, Valenza G. Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: a brain-heart study on thermal stress. Neuroimage 2022; 251:119023. [PMID: 35217203 DOI: 10.1016/j.neuroimage.2022.119023] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 12/12/2022] Open
Abstract
The study of functional brain-heart interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control.
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Affiliation(s)
- Diego Candia-Rivera
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy.
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics, and Bioengineering, Politecnico di Milano, 20133, Milano, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy
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Lin Y, Xiao Y, Wang L, Guo Y, Zhu W, Dalip B, Kamarthi S, Schreiber KL, Edwards RR, Urman RD. Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals. Front Neurosci 2022; 16:831627. [PMID: 35221908 PMCID: PMC8874020 DOI: 10.3389/fnins.2022.831627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/07/2022] [Indexed: 11/17/2022] Open
Abstract
Optimization of pain assessment and treatment is an active area of research in healthcare. The purpose of this research is to create an objective pain intensity estimation system based on multimodal sensing signals through experimental studies. Twenty eight healthy subjects were recruited at Northeastern University. Nine physiological modalities were utilized in this research, namely facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), and blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), blood pressure (BP). Statistical analysis and machine learning algorithms were deployed to analyze the physiological data. FE, EEG, SC, BVP, and BP proved to be able to detect different pain states from healthy subjects. Multi-modalities proved to be promising in detecting different levels of painful states. A decision-level multi-modal fusion also proved to be efficient and accurate in classifying painful states.
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Affiliation(s)
- Yingzi Lin
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
- *Correspondence: Yingzi Lin,
| | - Yan Xiao
- College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX, United States
| | - Li Wang
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Yikang Guo
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Wenchao Zhu
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Biren Dalip
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Sagar Kamarthi
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Kristin L. Schreiber
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, United States
| | - Robert R. Edwards
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, United States
| | - Richard D. Urman
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, United States
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