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Medani M, Alsubai S, Min H, Dutta AK, Anjum M. Discriminant Input Processing Scheme for Self-Assisted Intelligent Healthcare Systems. Bioengineering (Basel) 2024; 11:715. [PMID: 39061797 PMCID: PMC11274065 DOI: 10.3390/bioengineering11070715] [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: 06/09/2024] [Revised: 07/06/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method's scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management's accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations.
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Affiliation(s)
- Mohamed Medani
- Applied College of Mahail Aseer, King Khalid University, Abha 62529, Saudi Arabia
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 16278, Saudi Arabia
| | - Hong Min
- School of Computing, Gachon University, Seongnam 13120, Republic of Korea
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia
| | - Mohd Anjum
- Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India;
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2
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Rahman R, Talukder A, Das S, Saha J, Sarma H. Understanding and predicting pregnancy termination in Bangladesh: A comprehensive analysis using a hybrid machine learning approach. Medicine (Baltimore) 2024; 103:e38709. [PMID: 38941421 DOI: 10.1097/md.0000000000038709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/30/2024] Open
Abstract
Reproductive health issues, including unsafe pregnancy termination, remain a significant concern for women in developing nations. This study focused on investigating and predicting pregnancy termination in Bangladesh by employing a hybrid machine learning approach. The analysis used data from the Bangladesh Demographic and Health Surveys conducted in 2011, 2014, and 2017 to 2018. Ten independent variables, encompassing factors such as age, residence, division, wealth index, working status, BMI, total number of children ever born, recent births, and number of living children, were examined for their potential associations with pregnancy termination. The dataset undergoes preprocessing, addressing missing values and balancing class distributions. To predict pregnancy termination, 8 classical machine learning models and hybrid models were used in this study. The models' performance was evaluated based on the area under the curve, precision, recall, and F1 score. The results highlighted the effectiveness of the hybrid models, particularly the Voting hybrid model (area under the curve: 91.97; precision: 84.14; recall: 83.87; F1 score: 83.84), in accurately predicting pregnancy termination. Notable predictors include age, division, and wealth index. These findings hold significance for policy interventions aiming to reduce pregnancy termination rates, emphasizing the necessity for tailored approaches that consider regional disparities and socioeconomic factors. Overall, the study demonstrates the efficacy of hybrid machine learning models in comprehending and forecasting pregnancy termination, offering valuable insights for reproductive health initiatives in Bangladesh and similar contexts.
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Affiliation(s)
- Riaz Rahman
- Statistics Discipline, Science Engineering and Technology School, Khulna University, Khulna, Bangladesh
| | - Ashis Talukder
- Statistics Discipline, Science Engineering and Technology School, Khulna University, Khulna, Bangladesh
- Applied Epidemiology, National Centre for Epidemiology and Population Health, Australian National University, Acton, ACT, Australia
| | - Shatabdi Das
- Statistics Discipline, Science Engineering and Technology School, Khulna University, Khulna, Bangladesh
| | - Joy Saha
- Statistics Discipline, Science Engineering and Technology School, Khulna University, Khulna, Bangladesh
| | - Haribondhu Sarma
- Applied Epidemiology, National Centre for Epidemiology and Population Health, Australian National University, Acton, ACT, Australia
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3
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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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4
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Risqiwati D, Wibawa AD, Pane ES, Yuniarno EM, Islamiyah WR, Purnomo MH. Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation. Brain Inform 2024; 11:12. [PMID: 38740660 DOI: 10.1186/s40708-024-00225-y] [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: 04/17/2023] [Accepted: 04/17/2024] [Indexed: 05/16/2024] Open
Abstract
A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient's relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data.
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Affiliation(s)
- Diah Risqiwati
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Departement of Informatics, Universitas Muhammadiyah Malang, Tlogomas, Malang, 65144, Indonesia
| | - Adhi Dharma Wibawa
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Medical Technology, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
| | - Evi Septiana Pane
- Industrial Training and Education of Surabaya, Ministry of Industry RI, Gayungan, Surabaya, 60235, Indonesia
| | - Eko Mulyanto Yuniarno
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Departement of Computer Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
| | - Wardah Rahmatul Islamiyah
- Neurology Department, Faculty of Medicine, Universitas Airlangga, Gubeng, Surabaya, 60131, Indonesia
| | - Mauridhi Hery Purnomo
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia.
- Departement of Computer Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia.
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5
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Akhand MAH, Maria MA, Kamal MAS, Murase K. Improved EEG-based emotion recognition through information enhancement in connectivity feature map. Sci Rep 2023; 13:13804. [PMID: 37612354 PMCID: PMC10447430 DOI: 10.1038/s41598-023-40786-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/16/2023] [Indexed: 08/25/2023] Open
Abstract
Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs' measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods.
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Affiliation(s)
- M A H Akhand
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
| | - Mahfuza Akter Maria
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Md Abdus Samad Kamal
- Graduate School of Science and Technology, Gunma University, Kiryu, 376-8515, Japan
| | - Kazuyuki Murase
- Department of Information Technology, International Professional University of Technology in Osaka, 3-3-1 Umeda, Kita-ku, Osaka, 530-0001, Japan
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6
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Mouri FI, Valderrama CE, Camorlinga SG. Identifying relevant asymmetry features of EEG for emotion processing. Front Psychol 2023; 14:1217178. [PMID: 37663334 PMCID: PMC10469865 DOI: 10.3389/fpsyg.2023.1217178] [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: 05/04/2023] [Accepted: 07/21/2023] [Indexed: 09/05/2023] Open
Abstract
The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition.
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7
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Nandini D, Yadav J, Rani A, Singh V. Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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8
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Gong L, Li M, Zhang T, Chen W. EEG emotion recognition using attention-based convolutional transformer neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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9
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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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10
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Emotional State Classification from MUSIC-Based Features of Multichannel EEG Signals. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010099. [PMID: 36671671 PMCID: PMC9854769 DOI: 10.3390/bioengineering10010099] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/06/2023] [Indexed: 01/14/2023]
Abstract
Electroencephalogram (EEG)-based emotion recognition is a computationally challenging issue in the field of medical data science that has interesting applications in cognitive state disclosure. Generally, EEG signals are classified from frequency-based features that are often extracted using non-parametric models such as Welch's power spectral density (PSD). These non-parametric methods are not computationally sound due to having complexity and extended run time. The main purpose of this work is to apply the multiple signal classification (MUSIC) model, a parametric-based frequency-spectrum-estimation technique to extract features from multichannel EEG signals for emotional state classification from the SEED dataset. The main challenge of using MUSIC in EEG feature extraction is to tune its parameters for getting the discriminative features from different classes, which is a significant contribution of this work. Another contribution is to show some flaws of this dataset for the first time that contributed to achieving high classification accuracy in previous research works. This work used MUSIC features to classify three emotional states and achieve 97% accuracy on average using an artificial neural network. The proposed MUSIC model optimizes a 95-96% run time compared with the conventional classical non-parametric technique (Welch's PSD) for feature extraction.
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11
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Wang J, Xu Y, Tian J, Li H, Jiao W, Sun Y, Li G. Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1715. [PMID: 36554120 PMCID: PMC9777516 DOI: 10.3390/e24121715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/12/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
Driving fatigue is the main cause of traffic accidents, which seriously affects people's life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection.
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Affiliation(s)
- Jie Wang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Yanting Xu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Jinghong Tian
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Huayun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua 321004, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
| | - Weidong Jiao
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310058, China
| | - Gang Li
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310058, China
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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12
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Zhou J, Zhao T, Xie Y, Xiao F, Sun L. Emotion Recognition Based on Brain Connectivity Reservoir and Valence Lateralization for Cyber-Physical-Social Systems. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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13
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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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14
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Yang H, Huang S, Guo S, Sun G. Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition. ENTROPY 2022; 24:e24050705. [PMID: 35626587 PMCID: PMC9141183 DOI: 10.3390/e24050705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers’ output probabilities as a portion of the weighted features.
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Affiliation(s)
- Haihui Yang
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
| | - Shiguo Huang
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
| | - Shengwei Guo
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
| | - Guobing Sun
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
- Correspondence: ; Tel.: +86-18946119665
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15
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Houssein EH, Hammad A, Ali AA. Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07292-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractAffective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.
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Pusarla AN, Singh BA, Tripathi CS. Learning DenseNet features from EEG based spectrograms for subject independent emotion recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103485] [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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Li R, Ren C, Zhang X, Hu B. A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. Comput Biol Med 2022; 140:105080. [PMID: 34902609 DOI: 10.1016/j.compbiomed.2021.105080] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 12/19/2022]
Abstract
Emotion recognition is a vital but challenging step in creating passive brain-computer interface applications. In recent years, many studies on electroencephalogram (EEG)-based emotion recognition have been conducted. Ensemble learning has been widely used in emotion recognition because of its superior accuracy and generalization. In this study, we proposed a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. First, we used a 4 s sliding time window with a 2 s overlap to extract 13 different features from EEG signals and construct a feature vector. Then, we employed L1 regularization to select effective features. Second, a model selection method was applied to choose the optimal basic analysis submodels. Afterward, we proposed an ensemble operator that converts the classification results of a single model from discrete values to continuous values to better characterize the classification results. Subsequently, multiple objective particle swarm optimization was adopted to confirm the optimal parameters of the ensemble learning model. Finally, we conducted extensive experiments on two public datasets: DEAP and SEED. Considering the generalization of the model, we applied leave-one-subject-out cross-validation to evaluate the performance of the model. The experimental results demonstrate that the proposed method achieves a better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22%, respectively, on the DEAP database, and the average accuracy on the SEED database is 84.44%.
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Affiliation(s)
- Rui Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Chao Ren
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Xiaowei Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
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Cai J, Xiao R, Cui W, Zhang S, Liu G. Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review. Front Syst Neurosci 2021; 15:729707. [PMID: 34887732 PMCID: PMC8649925 DOI: 10.3389/fnsys.2021.729707] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 11/08/2021] [Indexed: 11/13/2022] Open
Abstract
Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.
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Affiliation(s)
- Jing Cai
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Ruolan Xiao
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Wenjie Cui
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Shang Zhang
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Guangda Liu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
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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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Wang J, Wang M. Review of the emotional feature extraction and classification using EEG signals. COGNITIVE ROBOTICS 2021. [DOI: 10.1016/j.cogr.2021.04.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Wei Y, Zhou J, Wang Y, Liu Y, Liu Q, Luo J, Wang C, Ren F, Huang L. A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:145-163. [PMID: 32078560 DOI: 10.1109/tbcas.2020.2974154] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-Based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-Based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-Based biomedical processor have also been discussed.
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