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Goshvarpour A, Goshvarpour A. Lemniscate of Bernoulli's map quantifiers: innovative measures for EEG emotion recognition. Cogn Neurodyn 2024; 18:1061-1077. [PMID: 38826652 PMCID: PMC11143135 DOI: 10.1007/s11571-023-09968-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/18/2023] [Accepted: 04/05/2023] [Indexed: 06/04/2024] Open
Abstract
Thanks to the advent of affective computing, designing an automatic human emotion recognition system for clinical and non-clinical applications has attracted the attention of many researchers. Currently, multi-channel electroencephalogram (EEG)-based emotion recognition is a fundamental but challenging issue. This experiment envisioned developing a new scheme for automated EEG affect recognition. An innovative nonlinear feature engineering approach was presented based on Lemniscate of Bernoulli's Map (LBM), which belongs to the family of chaotic maps, in line with the EEG's nonlinear nature. As far as the authors know, LBM has not been utilized for biological signal analysis. Next, the map was characterized using several graphical indices. The feature vector was imposed on the feature selection algorithm while evaluating the role of the feature vector dimension on emotion recognition rates. Finally, the efficiency of the features on emotion recognition was appraised using two conventional classifiers and validated using the Database for Emotion Analysis using Physiological signals (DEAP) and SJTU Emotion EEG Dataset-IV (SEED-IV) benchmark databases. The experimental results showed a maximum accuracy of 92.16% for DEAP and 90.7% for SEED-IV. Achieving higher recognition rates compared to the state-of-art EEG emotion recognition systems suggest the proposed method based on LBM could have potential both in characterizing bio-signal dynamics and detecting affect-deficit disorders.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan Iran
- Health Technology Research Center, Imam Reza International University, Mashhad, Razavi Khorasan Iran
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2
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EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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3
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Goshvarpour A, Goshvarpour A. Emotion Recognition Using a Novel Granger Causality Quantifier and Combined Electrodes of EEG. Brain Sci 2023; 13:brainsci13050759. [PMID: 37239231 DOI: 10.3390/brainsci13050759] [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: 04/12/2023] [Revised: 04/30/2023] [Accepted: 05/02/2023] [Indexed: 05/28/2023] Open
Abstract
Electroencephalogram (EEG) connectivity patterns can reflect neural correlates of emotion. However, the necessity of evaluating bulky data for multi-channel measurements increases the computational cost of the EEG network. To date, several approaches have been presented to pick the optimal cerebral channels, mainly depending on available data. Consequently, the risk of low data stability and reliability has increased by reducing the number of channels. Alternatively, this study suggests an electrode combination approach in which the brain is divided into six areas. After extracting EEG frequency bands, an innovative Granger causality-based measure was introduced to quantify brain connectivity patterns. The feature was subsequently subjected to a classification module to recognize valence-arousal dimensional emotions. A Database for Emotion Analysis Using Physiological Signals (DEAP) was used as a benchmark database to evaluate the scheme. The experimental results revealed a maximum accuracy of 89.55%. Additionally, EEG-based connectivity in the beta-frequency band was able to effectively classify dimensional emotions. In sum, combined EEG electrodes can efficiently replicate 32-channel EEG information.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz 51335-1996, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Mashhad 91388-3186, Iran
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4
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Li Z, Zhang G, Wang L, Wei J, Dang J. Emotion recognition using spatial-temporal EEG features through convolutional graph attention network. J Neural Eng 2023; 20. [PMID: 36720164 DOI: 10.1088/1741-2552/acb79e] [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: 11/12/2022] [Accepted: 01/31/2023] [Indexed: 02/02/2023]
Abstract
Objective.Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is significant for realizing emotional brain-computer interaction and improving machine intelligence.Approach.In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model.Main results.We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11% ± 0.83% and 94.83% ± 3.41% in the subject-dependent and subject-independent experiments on the SEED dataset, and achieved an accuracy of 91.19% ± 1.24% and 92.03% ± 4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art performance on cross-subject emotion recognition tasks for both datasets. In addition, we gained insight into the proposed frame through both the ablation experiments and the analysis of spatial patterns of FC and DE features.Significance.All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.
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Affiliation(s)
- Zhongjie Li
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300350, People's Republic of China
| | - Gaoyan Zhang
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300350, People's Republic of China
| | - Longbiao Wang
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300350, People's Republic of China
| | - Jianguo Wei
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300350, People's Republic of China
| | - Jianwu Dang
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300350, People's Republic of China
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Bendrich N, Kumar P, Scheme E. Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition. SENSORS (BASEL, SWITZERLAND) 2022; 22:9282. [PMID: 36501983 PMCID: PMC9737269 DOI: 10.3390/s22239282] [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: 10/14/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
The monitoring of emotional state is important in the prevention and management of mental health problems and is increasingly being used to support affective computing. As such, researchers are exploring various modalities from which emotion can be inferred, such as through facial images or via electroencephalography (EEG) signals. Current research commonly investigates the performance of machine-learning-based emotion recognition systems by exposing users to stimuli that are assumed to elicit a single unchanging emotional response. Moreover, in order to demonstrate better results, many models are tested in evaluation frameworks that do not reflect realistic real-world implementations. Consequently, in this paper, we explore the design of EEG-based emotion recognition systems using longer, variable stimuli using the publicly available AMIGOS dataset. Feature engineering and selection results are evaluated across four different cross-validation frameworks, including versions of leave-one-movie-out (testing with a known user, but a previously unseen movie), leave-one-person-out (testing with a known movie, but a previously unseen person), and leave-one-person-and-movie-out (testing on both a new user and new movie). Results of feature selection lead to a 13% absolute improvement over comparable previously reported studies, and demonstrate the importance of evaluation framework on the design and performance of EEG-based emotion recognition systems.
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6
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Schizophrenia Diagnosis by Weighting the Entropy Measures of the Selected EEG Channel. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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7
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Dogan A, Barua PD, Baygin M, Tuncer T, Dogan S, Yaman O, Dogru AH, Acharya RU. Automated accurate emotion classification using Clefia pattern-based features with EEG signals. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2141694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Abdullah Dogan
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Orhan Yaman
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ali Hikmet Dogru
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Rajendra U. Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Garcia-Martinez B, Fernandez-Caballero A, Alcaraz R, Martinez-Rodrigo A. Application of Dispersion Entropy for the Detection of Emotions With Electroencephalographic Signals. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3099344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Beatriz Garcia-Martinez
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Antonio Fernandez-Caballero
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Raul Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Escuela Politécnica de Cuenca, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Arturo Martinez-Rodrigo
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Facultad de Comunicación, Instituto de Tecnologías Audiovisuales de Castilla-La Mancha, Universidad de Castilla-La Mancha, Cuenca, Spain
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Sun Y, Xu Y, Lv J, Liu Y. Self- and Situation-Focused Reappraisal are not homogeneous: Evidence from behavioral and brain networks. Neuropsychologia 2022; 173:108282. [PMID: 35660514 DOI: 10.1016/j.neuropsychologia.2022.108282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 05/13/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022]
Abstract
Reappraisal is an effective emotion regulation strategy which can be divided into self- and situation-focused subtypes. Previous studies have produced inconsistent findings on the moderating effects and neural mechanisms of reappraisal; thus, further research is necessary to clarify these inconsistencies. In this study, a total of 44 participants were recruited and randomly assigned to two groups. 23 participants were assigned to the self-focused group, while 21 participants were assigned to the situation-focused group. The participants' resting EEG data were collected for 6 minutes before the experiment began, followed by an emotional regulation task. During this task, participants were asked to view emotion-provoking images under four emotion regulation conditions (View, Watch, Increase, and Decrease). Late positive potential (LPP) was obtained when these emotional images were observed. LPP is an effective physiological indicator of emotion regulation, enabling this study to explore emotion regulation under different reappraisal strategies, as well as the functional connectivity and node efficiency within the brain. It was found that, in terms of the effect on emotion regulation, situation-focused reappraisal was significantly better than self-focused reappraisal at enhancing the valence of negative emotion, while self-focused reappraisal was significantly better than situation-focused reappraisal at increasing the arousal of negative emotion. In terms of neural mechanisms, multiple brain regions such as the anterior cingulate cortex, the frontal lobe, the parahippocampal gyrus, parts of the temporal lobe, and parts of the parietal lobe were involved in both reappraisal processes. In addition, there were some differences in brain regions associated with different forms of cognitive reappraisal. Self-focused reappraisal was associated with the posterior cingulate gyrus, fusiform gyrus, and lingual gyrus, and situation-focused reappraisal was associated with the parietal lobule, anterior central gyrus, and angular gyrus. In conclusion, this research demonstrates that self- and situation-focused reappraisal are not homogenous in terms of their effects and neural mechanisms and clarifies the uncertainties over their regulatory effects. Different types of reappraisal activate different brain regions when used, and the functional connectivity or node efficiency of these brain regions seems to be a suitable indicator for assessing the effects of different types of reappraisal.
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Affiliation(s)
- Yan Sun
- School of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Yuanyuan Xu
- School of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Jiaojiao Lv
- School of Psychology, Liaoning Normal University, Dalian, 116029, China; Department of Psychology, Shanxi Datong University, Datong, 037009, China
| | - Yan Liu
- School of Psychology, Liaoning Normal University, Dalian, 116029, China.
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10
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Goshvarpour A, Goshvarpour A. Innovative Poincare's plot asymmetry descriptors for EEG emotion recognition. Cogn Neurodyn 2022; 16:545-559. [PMID: 35603058 PMCID: PMC9120274 DOI: 10.1007/s11571-021-09735-5] [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: 02/07/2021] [Revised: 09/18/2021] [Accepted: 10/13/2021] [Indexed: 10/20/2022] Open
Abstract
Given the importance of emotion recognition in both medical and non-medical applications, designing an automatic system has captured the attention of several scholars. Currently, EEG-based emotion recognition has a special position, which has not fulfilled the desired accuracy rates yet. This experiment intended to provide novel EEG asymmetry measures to improve emotion recognition rates. Four emotional states have been classified using the k-nearest neighbor (kNN), support vector machine, and Naïve Bayes. Feature selection has been performed, and the role of employing a different number of top-ranked features on emotion recognition rates has been assessed. To validate the efficiency of the proposed scheme, two public databases, including the SJTU Emotion EEG Dataset-IV (SEED-IV) and a Database for Emotion Analysis using Physiological signals (DEAP) were evaluated. The experimental results indicated that kNN outperformed the other classifiers with a maximum accuracy of 95.49 and 98.63% using SEED-IV and DEAP datasets, respectively. In conclusion, the results of the proposed novel EEG-asymmetry measures make the framework a superior one compared to the state-of-art EEG emotion recognition approaches.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Rezvan Campus, Phalestine Sq., Mashhad, Razavi Khorasan Iran
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11
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Goshvarpour A, Goshvarpour A. Verhulst map measures: new biomarkers for heart rate classification. Phys Eng Sci Med 2022; 45:513-523. [PMID: 35303265 DOI: 10.1007/s13246-022-01117-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/08/2022] [Indexed: 12/16/2022]
Abstract
Recording, monitoring, and analyzing biological signals has received significant attention in medicine. A fundamental phase for understanding a bio-system under various conditions is to process the corresponding bio-signal appropriately. To this effect, different conventional and nonlinear approaches have been proposed. However, since the non-stationary properties of the bio-signals are not revealed by traditional linear methods, nonlinear dynamical techniques play a crucial role in examining the behavior of a bio-system. This work proposes new bio-markers based on the chaotic nature of the biomedical signals. These measures were introduced using the Verhulst map, a simple tool for characterizing the morphology of the reconstructed phase space. For this purpose, we extracted the features from the heart rate (HR) signals of six groups of meditators and non-meditators. For a typical classification problem, the performance of some conventional classifiers, including the k-nearest neighbor, support vector machine, and Naïve Bayes, was appraised separately. In addition, the competence of a hybrid classification strategy was inspected using majority voting. The results indicated a maximum accuracy, F1-score, and sensitivity of 100%. These findings reveal that the proposed framework is eminently capable of analyzing and classifying the HR signals of the groups. In conclusion, the Verhulst diagram-based measures are simple and based on the dynamics of the bio-signals, which can be served for quantifying different signals in medical systems.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran. .,Health Technology Research Center, Imam Reza International University, Mashhad, Razavi Khorasan, Iran.
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12
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Gao Z, Cui X, Wan W, Zheng W, Gu Z. Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103291] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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13
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Goshvarpour A, Goshvarpour A. Human Emotion Recognition using Polar-Based Lagged Poincare Plot Indices of Eye-Blinking Data. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2021. [DOI: 10.1142/s1469026821500231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Emotion recognition using bio-signals is currently a hot and challenging topic in human–computer interferences, robotics, and affective computing. A broad range of literature has been published by analyzing the internal/external behaviors of the subjects in confronting emotional events/stimuli. Eye movements, as an external behavior, are frequently used in the multi-modal emotion recognition system. On the other hand, classic statistical features of the signal have generally been assessed, and the evaluation of its dynamics has been neglected so far. For the first time, the dynamics of single-modal eye-blinking data are characterized. Novel polar-based indices of the lagged Poincare plots were introduced. The optimum lag was estimated using mutual information. After reconstruction of the plot, the polar measures of all points were characterized using statistical measures. The support vector machine (SVM), decision tree, and Naïve Bayes were implemented to complete the process of classification. The highest accuracy of 100% with an average accuracy of 84.17% was achieved for fear/sad discrimination using SVM. The suggested framework provided outstanding performances in terms of recognition rates, simplicity of the methodology, and less computational cost. Our results also showed that eye-blinking data possesses the potential for emotion recognition, especially in classifying fear emotion.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran
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Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05624-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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15
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Varone G, Hussain Z, Sheikh Z, Howard A, Boulila W, Mahmud M, Howard N, Morabito FC, Hussain A. Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures. SENSORS 2021; 21:s21020637. [PMID: 33477526 PMCID: PMC7831109 DOI: 10.3390/s21020637] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 01/24/2023]
Abstract
Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.
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Affiliation(s)
- Giuseppe Varone
- Department of Medical and Surgical Sciences, Magna Greacia University of Catanzaro, 88100 Catanzaro, Italy;
| | - Zain Hussain
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4TJ, UK; (Z.H.); (Z.S.)
- Howard Brain Sciences Foundation, Providence, RI 02906, USA;
| | - Zakariya Sheikh
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4TJ, UK; (Z.H.); (Z.S.)
| | - Adam Howard
- Howard Brain Sciences Foundation, Providence, RI 02906, USA;
| | - Wadii Boulila
- RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia;
- IS Department, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia
| | - Mufti Mahmud
- Department of Computer Science and Medical Technology Innovation Facility, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK;
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK;
| | | | - Amir Hussain
- School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK;
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Peng Y, Qin F, Kong W, Ge Y, Nie F, Cichocki A. GFIL: A Unified Framework for the Importance Analysis of Features, Frequency Bands and Channels in EEG-based Emotion Recognition. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3082803] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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17
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Ramirez-Quintana JA, Madrid-Herrera L, Chacon-Murguia MI, Corral-Martinez LF. Brain-Computer Interface System Based on P300 Processing with Convolutional Neural Network, Novel Speller, and Low Number of Electrodes. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09744-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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18
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Evaluation of Novel Entropy-Based Complex Wavelet Sub-bands Measures of PPG in an Emotion Recognition System. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00526-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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