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Kamal SM, Babini MH, Tee R, Krejcar O, Namazi H. Decoding the correlation between heart activation and walking path by information-based analysis. Technol Health Care 2023; 31:205-215. [PMID: 35848002 DOI: 10.3233/thc-220191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROND One of the important areas of heart research is to analyze heart rate variability during (HRV) walking. OBJECTIVE In this research, we investigated the correction between heart activation and the variations of walking paths. METHOD We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation. RESULTS According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series. CONCLUSION The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.
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Affiliation(s)
| | | | - Rui Tee
- School of Pharmacy, Monash University, Selangor, Malaysia
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic.,Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Hamidreza Namazi
- School of Engineering, Monash University, Selangor, Malaysia.,Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
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2
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Wang R, Wang H, Shi L, Han C, Che Y. Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1540. [PMID: 36359630 PMCID: PMC9689850 DOI: 10.3390/e24111540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.
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Affiliation(s)
- Ruofan Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Haodong Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Lianshuan Shi
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Chunxiao Han
- Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Yanqiu Che
- Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
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3
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Liu J, Sun L, Liu J, Huang M, Xu Y, Li R. Enhancing Emotion Recognition Using Region-Specific Electroencephalogram Data and Dynamic Functional Connectivity. Front Neurosci 2022; 16:884475. [PMID: 35585922 PMCID: PMC9108496 DOI: 10.3389/fnins.2022.884475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/13/2022] [Indexed: 11/13/2022] Open
Abstract
Recognizing the emotional states of humans through EEG signals are of great significance to the progress of human-computer interaction. The present study aimed to perform automatic recognition of music-evoked emotions through region-specific information and dynamic functional connectivity of EEG signals and a deep learning neural network. EEG signals of 15 healthy volunteers were collected when different emotions (high-valence-arousal vs. low-valence-arousal) were induced by a musical experimental paradigm. Then a sequential backward selection algorithm combining with deep neural network called Xception was proposed to evaluate the effect of different channel combinations on emotion recognition. In addition, we also assessed whether dynamic functional network of frontal cortex, constructed through different trial number, may affect the performance of emotion cognition. Results showed that the binary classification accuracy based on all 30 channels was 70.19%, the accuracy based on all channels located in the frontal region was 71.05%, and the accuracy based on the best channel combination in the frontal region was 76.84%. In addition, we found that the classification performance increased as longer temporal functional network of frontal cortex was constructed as input features. In sum, emotions induced by different musical stimuli can be recognized by our proposed approach though region-specific EEG signals and time-varying functional network of frontal cortex. Our findings could provide a new perspective for the development of EEG-based emotional recognition systems and advance our understanding of the neural mechanism underlying emotion processing.
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Affiliation(s)
- Jun Liu
- College of Information Engineering, Nanchang Hangkong University, Nanchang, China
| | - Lechan Sun
- College of Information Engineering, Nanchang Hangkong University, Nanchang, China
| | - Jun Liu
- College of Aviation Service and Music, Nanchang Hangkong University, Nanchang, China
| | - Min Huang
- College of Aviation Service and Music, Nanchang Hangkong University, Nanchang, China
| | - Yichen Xu
- College of Aviation Service and Music, Nanchang Hangkong University, Nanchang, China
| | - Rihui Li
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University, Stanford, CA, United States
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4
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Soundirarajan M, Kuca K, Krejcar O, Namazi H. Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation. Technol Health Care 2021; 30:859-868. [PMID: 34842201 DOI: 10.3233/thc-213528] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science. OBJECTIVE In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy. METHOD The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals. RESULTS The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals. CONCLUSION The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.
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Affiliation(s)
| | - Kamil Kuca
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Czechia.,Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Czechia.,Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Hamidreza Namazi
- School of Engineering, Monash University, Selangor, Malaysia.,Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Czechia
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5
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Zhang XN, Meng QH, Zeng M, Hou HR. Decoding olfactory EEG signals for different odor stimuli identification using wavelet-spatial domain feature. J Neurosci Methods 2021; 363:109355. [PMID: 34506866 DOI: 10.1016/j.jneumeth.2021.109355] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/11/2021] [Accepted: 09/05/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Decoding olfactory-induced electroencephalography (olfactory EEG) signals has gained significant attention in recent years, owing to its potential applications in several fields, such as disease diagnosis, multimedia applications, and brain-computer interaction (BCI). Extracting discriminative features from olfactory EEG signals with low spatial resolution and poor signal-to-noise ratio is vital but challenging for improving decoding accuracy. NEW METHODS By combining discrete wavelet transform (DWT) with one-versus-rest common spatial pattern (OVR-CSP), we develop a novel feature, named wavelet-spatial domain feature (WSDF), to decode the olfactory EEG signals. First, DWT is employed on EEG signals for multilevel wavelet decomposition. Next, the DWT coefficients obtained at a specific level are subjected to OVR-CSP for spatial filtering. Correspondingly, the variance is extracted to generate a discriminative feature set, labeled as WSDF. RESULTS To verify the effectiveness of WSDF, a classification of olfactory EEG signals was conducted on two data sets, i.e., a public EEG dataset 'Odor Pleasantness Perception Dataset (OPPD)', and a self-collected dataset, by using support vector machine (SVM) trained based on different cross-validation methods. Experimental results showed that on OPPD dataset, the proposed method achieved a best average accuracy of 100% and 94.47% for the eyes-open and eyes-closed conditions, respectively. Moreover, on our own dataset, the proposed method gave a highest average accuracy of 99.50%. COMPARISON WITH EXISTING METHODS Compared with a wide range of EEG features and existing works on the same dataset, our WSDF yielded superior classification performance. CONCLUSIONS The proposed WSDF is a promising candidate for decoding olfactory EEG signals.
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Affiliation(s)
- Xiao-Nei Zhang
- Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Qing-Hao Meng
- Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Ming Zeng
- Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
| | - Hui-Rang Hou
- Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
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6
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Pakniyat N, Namazi H. Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations. Front Hum Neurosci 2021; 15:749082. [PMID: 34690727 PMCID: PMC8531105 DOI: 10.3389/fnhum.2021.749082] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/06/2021] [Indexed: 11/18/2022] Open
Abstract
In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.
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Affiliation(s)
| | - Hamidreza Namazi
- Incubator of Kinanthropology Research, Faculty of Sports Studies, Masaryk University, Brno, Czechia.,College of Engineering and Science, Victoria University, Melbourne, VIC, Australia
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7
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Liu H, Zhang Y, Li Y, Kong X. Review on Emotion Recognition Based on Electroencephalography. Front Comput Neurosci 2021; 15:758212. [PMID: 34658828 PMCID: PMC8518715 DOI: 10.3389/fncom.2021.758212] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 08/31/2021] [Indexed: 11/13/2022] Open
Abstract
Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.
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Affiliation(s)
- Haoran Liu
- The Boiler and Pressure Vessel Safety Inspection Institute of Henan Province, Zhengzhou, China
| | - Ying Zhang
- Patent Examination Cooperation (Henan) Center of the Patent Office, CNIPA, Zhengzhou, China
| | - Yujun Li
- The Boiler and Pressure Vessel Safety Inspection Institute of Henan Province, Zhengzhou, China
| | - Xiangyi Kong
- The Boiler and Pressure Vessel Safety Inspection Institute of Henan Province, Zhengzhou, China
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8
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Pakniyat N, Namazi H. Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals. Technol Health Care 2021; 30:623-632. [PMID: 34542048 DOI: 10.3233/thc-213052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.
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9
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Kamal SM, Dawi NBM, Sim S, Tee R, Nathan V, Aghasian E, Namazi H. Information-based analysis of the relation between human muscle reaction and walking path. Technol Health Care 2021; 28:675-684. [PMID: 32200366 DOI: 10.3233/thc-192034] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Walking is one of the important actions of the human body. For this purpose, the human brain communicates with leg muscles through the nervous system. Based on the walking path, leg muscles act differently. Therefore, there should be a relation between the activity of leg muscles and the path of movement. OBJECTIVE In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement. METHOD Since the electromyography (EMG) signal is a feature of muscle activity and the movement path has complex structures, we used entropy analysis in order to link their structures. The Shannon entropy of EMG signal and walking path are computed to relate their information content. RESULTS Based on the obtained results, walking on a path with greater information content causes greater information content in the EMG signal which is supported by statistical analysis results. This allowed us to analyze the relation between muscle activity and walking path. CONCLUSION The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.
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Affiliation(s)
| | | | - Sue Sim
- School of Engineering, Monash University, Selangor, Malaysia
| | - Rui Tee
- School of Pharmacy, Monash University, Selangor, Malaysia
| | - Visvamba Nathan
- School of Engineering, Monash University, Selangor, Malaysia
| | - Erfan Aghasian
- Discipline of ICT, School of Technology, Environments and Design, University of Tasmania, Hobart, Australia
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10
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Pakniyat N, Babini MH, Kulish VV, Namazi H. Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation. Technol Health Care 2021; 30:661-671. [PMID: 34397441 DOI: 10.3233/thc-213136] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
BACKGROUND Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart's activity, a relationship should exist among their activities. OBJECTIVE In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION We conclude that heart and brain activities are related.
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Affiliation(s)
| | | | - Vladimir V Kulish
- Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic
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11
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A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task. ENTROPY 2021; 23:e23080931. [PMID: 34441071 PMCID: PMC8394714 DOI: 10.3390/e23080931] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 07/15/2021] [Indexed: 12/13/2022]
Abstract
Properly measuring the complexity of time series is an important issue. The permutation entropy (PE) is a widely used as an effective complexity measurement algorithm, but it is not suitable for the complexity description of multi-dimensional data. In this paper, in order to better measure the complexity of multi-dimensional time series, we proposed a modified multivariable PE (MMPE) algorithm with principal component analysis (PCA) dimensionality reduction, which is a new multi-dimensional time series complexity measurement algorithm. The analysis results of different chaotic systems verify that MMPE is effective. Moreover, we applied it to the comlexity analysis of EEG data. It shows that the person during mental arithmetic task has higher complexity comparing with the state before mental arithmetic task. In addition, we also discussed the necessity of the PCA dimensionality reduction.
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Kamal SM, Dawi NM, Namazi H. Information-based decoding of the coupling among human brain activity and movement paths. Technol Health Care 2021; 29:1109-1118. [PMID: 33749623 DOI: 10.3233/thc-202744] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.
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13
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Ahamed MRA, Babini MH, Namazi H. Analysis of the information transfer between brains during a conversation. Technol Health Care 2021; 29:283-293. [DOI: 10.3233/thc-202366] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND: The interaction between people is one of the usual daily activities. For this purpose, people mainly connect with others, using their voice. Voices act as the auditory stimuli on the brain during a conversation. OBJECTIVE: In this research, we analyze the relationship between the brains’ activities of subjects during a conversation. METHODS: Since human voice transfers information from one subject to another, we used information theory for our analysis. We investigated the alterations of Shannon entropy of electroencephalography (EEG) signals for subjects during a conversation. RESULTS: The results demonstrated that the alterations in the information contents of the EEG signals for the listeners and speakers are correlated. Therefore, we concluded that the brains’ activities of both subjects are linked. CONCLUSION: Our results can be expanded to analyze the coupling among other physiological signals of subjects (such as heart rate) during the conversation.
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Soundirarajan M, Pakniyat N, Sim S, Nathan V, Namazi H. Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli. Technol Health Care 2021; 29:99-109. [DOI: 10.3233/thc-192085] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles. OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain. METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content). RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals. CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.
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Affiliation(s)
| | | | - Sue Sim
- School of Engineering, Monash University, Selangor, Malaysia
| | - Visvamba Nathan
- School of Engineering, Monash University, Selangor, Malaysia
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15
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Ahamed MRA, Babini MH, Namazi H. Complexity-based decoding of the relation between human voice and brain activity. Technol Health Care 2020; 28:665-674. [DOI: 10.3233/thc-192105] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND: The human voice is the main feature of human communication. It is known that the brain controls the human voice. Therefore, there should be a relation between the characteristics of voice and brain activity. OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed. METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed. RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours. CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.
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16
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Kamal SM, Sim S, Tee R, Nathan V, Aghasian E, Namazi H. Decoding of the relationship between human brain activity and walking paths. Technol Health Care 2020; 28:381-390. [DOI: 10.3233/thc-191965] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Sue Sim
- School of Engineering, Monash University, Selangor, Malaysia
| | - Rui Tee
- School of Pharmacy, Monash University, Selangor, Malaysia
| | - Visvamba Nathan
- School of Engineering, Monash University, Selangor, Malaysia
| | - Erfan Aghasian
- Discipline of ICT, School of Technology, Environments and Design, University of Tasmania, Hobart, Australia
| | - Hamidreza Namazi
- School of Engineering, Monash University, Selangor, Malaysia
- Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada
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