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Huang L, Du F, Huang W, Ren H, Qiu W, Zhang J, Wang Y. Three-stage Dynamic Brain-cognitive Model of Understanding Action Intention Displayed by Human Body Movements. Brain Topogr 2024:10.1007/s10548-024-01061-3. [PMID: 38874853 DOI: 10.1007/s10548-024-01061-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024]
Abstract
The ability to comprehend the intention conveyed through human body movements is crucial for effective interpersonal interactions. If people can't understand the intention behind other individuals' isolated or interactive actions, their actions will become meaningless. Psychologists have investigated the cognitive processes and neural representations involved in understanding action intention, yet a cohesive theoretical explanation remains elusive. Hence, we mainly review existing literature related to neural correlates of action intention, and primarily propose a putative Three-stage Dynamic Brain-cognitive Model of understanding action intention, which involves body perception, action identification and intention understanding. Specifically, at the first stage, body parts/shapes are processed by those brain regions such as extrastriate and fusiform body areas; During the second stage, differentiating observed actions relies on configuring relationships between body parts, facilitated by the activation of the Mirror Neuron System; The last stage involves identifying various intention categories, utilizing the Mentalizing System for recruitment, and different activation patterns concerning the nature of the intentions participants dealing with. Finally, we delves into the clinical practice, like intervention training based on a theoretical model for individuals with autism spectrum disorders who encounter difficulties in interpersonal communication.
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Affiliation(s)
- Liang Huang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China.
- Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
| | - Fangyuan Du
- Fuzhou University of International Studies and Trade, Fuzhou, China
| | - Wenxin Huang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Hanlin Ren
- Third People's Hospital of Zhongshan, Zhongshan, China
| | - Wenzhen Qiu
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
| | - Jiayi Zhang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
| | - Yiwen Wang
- The School of Economics and Management, Fuzhou University, Fuzhou, China.
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Belkacem AN, Nishio S, Suzuki T, Ishiguro H, Hirata M. Neuromagnetic Decoding of Simultaneous Bilateral Hand Movements for Multidimensional Brain-Machine Interfaces. IEEE Trans Neural Syst Rehabil Eng 2019; 26:1301-1310. [PMID: 29877855 DOI: 10.1109/tnsre.2018.2837003] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
To provide multidimensional control, we describe the first reported decoding of bilateral hand movements by using single-trial magnetoencephalography signals as a new approach to enhance a user's ability to interact with a complex environment through a multidimensional brain-machine interface. Ten healthy participants performed or imagined four types of bilateral hand movements during neuromagnetic measurements. By applying a support vector machine (SVM) method to classify the four movements regarding the sensor data obtained from the sensorimotor area, we found the mean accuracy of a two-class classification using the amplitudes of neuromagnetic fields to be particularly suitable for real-time applications, with accuracies comparable to those obtained in previous studies involving unilateral movement. The sensor data from over the sensorimotor cortex showed discriminative time-series waveforms and time-frequency maps in the bilateral hemispheres according to the four tasks. Furthermore, we used four-class classification algorithms based on the SVM method to decode all types of bilateral movements. Our results provided further proof that the slow components of neuromagnetic fields carry sufficient neural information to classify even bilateral hand movements and demonstrated the potential utility of decoding bilateral movements for engineering purposes such as multidimensional motor control.
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Filatova OG, Yang Y, Dewald JPA, Tian R, Maceira-Elvira P, Takeda Y, Kwakkel G, Yamashita O, van der Helm FCT. Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study. Front Neural Circuits 2018; 12:79. [PMID: 30327592 PMCID: PMC6174251 DOI: 10.3389/fncir.2018.00079] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/10/2018] [Indexed: 01/07/2023] Open
Abstract
In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.
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Affiliation(s)
- Olena G. Filatova
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - Yuan Yang
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Julius P. A. Dewald
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Runfeng Tian
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - Pablo Maceira-Elvira
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Clinical Neuroengineering, Centre for Neuroprosthetics, Swiss Federal Institute of Technology (EPFL), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Yusuke Takeda
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Neural Information Analysis Laboratories, ATR, Kyoto, Japan
| | - Gert Kwakkel
- Department of Rehabilitation Medicine, Amsterdam Neurosciences and Amsterdam Movement Sciences, University Medical Centre Amsterdam, Amsterdam, Netherlands
| | - Okito Yamashita
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Neural Information Analysis Laboratories, ATR, Kyoto, Japan
| | - Frans C. T. van der Helm
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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Decoding finger movement in humans using synergy of EEG cortical current signals. Sci Rep 2017; 7:11382. [PMID: 28900188 PMCID: PMC5595824 DOI: 10.1038/s41598-017-09770-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 07/31/2017] [Indexed: 12/31/2022] Open
Abstract
The synchronized activity of neuronal populations across multiple distant brain areas may reflect coordinated interactions of large-scale brain networks. Currently, there is no established method to investigate the temporal transitions between these large-scale networks that would allow, for example, to decode finger movements. Here we applied a matrix factorization method employing principal component and temporal independent component analyses to identify brain activity synchronizations. In accordance with previous studies investigating “muscle synergies”, we refer to this activity as “brain activity synergy”. Using electroencephalography (EEG), we first estimated cortical current sources (CSs) and then identified brain activity synergies within the estimated CS signals. A decoding analysis for finger movement in eight directions showed that such CS synergies provided more information for dissociating between movements than EEG sensor signals, EEG synergy, or CS signals, suggesting that temporal activation patterns of the synchronizing CSs may contain information related to motor control. A quantitative analysis of features selected by the decoders further revealed temporal transitions among the primary motor area, dorsal and ventral premotor areas, pre-supplementary motor area, and supplementary motor area, which may reflect transitions in motor planning and execution. These results provide a proof of concept for brain activity synergy estimation using CSs.
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