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Lyu S, Cheung RCC. Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine. SENSORS (BASEL, SWITZERLAND) 2023; 23:8976. [PMID: 37960675 PMCID: PMC10649020 DOI: 10.3390/s23218976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 10/29/2023] [Accepted: 11/03/2023] [Indexed: 11/15/2023]
Abstract
The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.
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Affiliation(s)
| | - Ray C. C. Cheung
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China;
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2
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Siddi S, Bailon R, Giné-Vázquez I, Matcham F, Lamers F, Kontaxis S, Laporta E, Garcia E, Lombardini F, Annas P, Hotopf M, Penninx BWJH, Ivan A, White KM, Difrancesco S, Locatelli P, Aguiló J, Peñarrubia-Maria MT, Narayan VA, Folarin A, Leightley D, Cummins N, Vairavan S, Ranjan Y, Rintala A, de Girolamo G, Simblett SK, Wykes T, Myin-Germeys I, Dobson R, Haro JM. The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity. Psychol Med 2023; 53:3249-3260. [PMID: 37184076 DOI: 10.1017/s0033291723001034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.
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Affiliation(s)
- S Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - R Bailon
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - I Giné-Vázquez
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - F Matcham
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- School of Psychology, University of Sussex, Falmer, UK
| | - F Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - S Kontaxis
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Laporta
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Garcia
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - F Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - P Annas
- H. Lundbeck A/S, Valby, Denmark
| | - M Hotopf
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - B W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - A Ivan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - K M White
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Difrancesco
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - P Locatelli
- Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy
| | - J Aguiló
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - M T Peñarrubia-Maria
- Catalan Institute of Health, Primary Care Research Institute (IDIAP Jordi Gol), CIBERESP, Barcelona, Spain
| | - V A Narayan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - A Folarin
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - D Leightley
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - N Cummins
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Vairavan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Y Ranjan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - A Rintala
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - G de Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - S K Simblett
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - T Wykes
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - I Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - R Dobson
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - J M Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
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3
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Russo C, Senese VP. Functional near-infrared spectroscopy is a useful tool for multi-perspective psychobiological study of neurophysiological correlates of parenting behaviour. Eur J Neurosci 2023; 57:258-284. [PMID: 36485015 DOI: 10.1111/ejn.15890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/02/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
The quality of the relationship between caregiver and child has long-term effects on the cognitive and socio-emotional development of children. A process involved in human parenting is the bio-behavioural synchrony that occurs between the partners in the relationship during interaction. Through interaction, bio-behavioural synchronicity allows the adaptation of the physiological systems of the parent to those of the child and promotes the positive development and modelling of the child's social brain. The role of bio-behavioural synchrony in building social bonds could be investigated using functional near-infrared spectroscopy (fNIRS). In this paper we have (a) highlighted the importance of the quality of the caregiver-child relationship for the child's cognitive and socio-emotional development, as well as the relevance of infantile stimuli in the activation of parenting behaviour; (b) discussed the tools used in the study of the neurophysiological substrates of the parental response; (c) proposed fNIRS as a particularly suitable tool for the study of parental responses; and (d) underlined the need for a multi-systemic psychobiological approach to understand the mechanisms that regulate caregiver-child interactions and their bio-behavioural synchrony. We propose to adopt a multi-system psychobiological approach to the study of parental behaviour and social interaction.
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Affiliation(s)
- Carmela Russo
- Psychometric Laboratory, Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy
| | - Vincenzo Paolo Senese
- Psychometric Laboratory, Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy
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4
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EEG based depression recognition using improved graph convolutional neural network. Comput Biol Med 2022; 148:105815. [DOI: 10.1016/j.compbiomed.2022.105815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/11/2022] [Accepted: 07/03/2022] [Indexed: 11/19/2022]
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5
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Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10042-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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6
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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7
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Lei Y, Belkacem AN, Wang X, Sha S, Wang C, Chen C. A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103370] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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8
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Hickey BA, Chalmers T, Newton P, Lin CT, Sibbritt D, McLachlan CS, Clifton-Bligh R, Morley J, Lal S. Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review. SENSORS 2021; 21:s21103461. [PMID: 34065620 PMCID: PMC8156923 DOI: 10.3390/s21103461] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/21/2022]
Abstract
Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
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Affiliation(s)
- Blake Anthony Hickey
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia; (B.A.H.); (T.C.)
| | - Taryn Chalmers
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia; (B.A.H.); (T.C.)
| | - Phillip Newton
- School of Nursing and Midwifery, Western Sydney University, Penrith, NSW 2747, Australia;
| | - Chin-Teng Lin
- Australian AI Institute, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia;
| | - David Sibbritt
- School of Public Health, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia;
| | - Craig S. McLachlan
- Centre for Healthy Futures, Torrens University, Sydney, NSW 2009, Australia;
| | - Roderick Clifton-Bligh
- Kolling Institute for Medical Research, Royal North Shore Hospital, St Leonards, NSW 2064, Australia;
| | - John Morley
- School of Medicine, Western Sydney University, Penrith, NSW 2747, Australia;
| | - Sara Lal
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia; (B.A.H.); (T.C.)
- Correspondence: ; Tel.: +612-9514-1592
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9
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Jiang C, Li Y, Tang Y, Guan C. Enhancing EEG-Based Classification of Depression Patients Using Spatial Information. IEEE Trans Neural Syst Rehabil Eng 2021; 29:566-575. [PMID: 33587703 DOI: 10.1109/tnsre.2021.3059429] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli. METHODS We proposed an effective electroencephalogram-based detection method for depression classification using spatial information. A face-in-the-crowd task, including positive and negative emotional facial expressions, was presented to 30 participants, including 16 depression patients and 14 healthy controls. Differential entropy and the genetic algorithm were used for feature extraction and selection, and a support vector machine was used for classification. A task-related common spatial pattern (TCSP) was proposed to enhance the spatial differences before the feature extraction. RESULTS AND DISCUSSION We achieved a leave-one-subject-out cross-validation classification result of 84% and 85.7% for positive and negative stimuli, respectively, using TCSP, which is statistically significantly higher than 81.7% and 83.2%, respectively, acquired without the TCSP (p < 0.05). We also evaluated the classification performance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluated different classifiers, including k-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP. CONCLUSION The results show that our proposed method, employing spatial information, significantly improves the accuracy of classifying depression patients.
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10
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Lau-Zhu A, Lau MPH, McLoughlin G. Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Dev Cogn Neurosci 2019; 36:100635. [PMID: 30877927 PMCID: PMC6534774 DOI: 10.1016/j.dcn.2019.100635] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 03/06/2019] [Accepted: 03/06/2019] [Indexed: 11/23/2022] Open
Abstract
Mobile electroencephalography (mobile EEG) represents a next-generation neuroscientific technology – to study real-time brain activity – that is relatively inexpensive, non-invasive and portable. Mobile EEG leverages state-of-the-art hardware alongside established advantages of traditional EEG and recent advances in signal processing. In this review, we propose that mobile EEG could open unprecedented possibilities for studying neurodevelopmental disorders. We first present a brief overview of recent developments in mobile EEG technologies, emphasising the proliferation of studies in several neuroscientific domains. As these developments have yet to be exploited by neurodevelopmentalists, we then identify three research opportunities: 1) increase in the ease and flexibility of brain data acquisition in neurodevelopmental populations; 2) integration into powerful developmentally-informative research designs; 3) development of innovative non-stationary EEG-based paradigms. Critically, we address key challenges that should be considered to fully realise the potential of mobile EEG for neurodevelopmental research and for understanding developmental psychopathology more broadly, and suggest future research directions.
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Affiliation(s)
- Alex Lau-Zhu
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Michael P H Lau
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Gráinne McLoughlin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Hou Y, Chen S. Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:3191903. [PMID: 30956655 PMCID: PMC6431402 DOI: 10.1155/2019/3191903] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 12/25/2018] [Accepted: 01/28/2019] [Indexed: 11/18/2022]
Abstract
Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.
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Affiliation(s)
- Yimin Hou
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
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12
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EEG-based mild depression recognition using convolutional neural network. Med Biol Eng Comput 2019; 57:1341-1352. [DOI: 10.1007/s11517-019-01959-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Accepted: 01/28/2019] [Indexed: 10/27/2022]
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13
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Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset. BIOMED RESEARCH INTERNATIONAL 2018; 2018:1049257. [PMID: 30671443 PMCID: PMC6323535 DOI: 10.1155/2018/1049257] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 12/06/2018] [Indexed: 11/18/2022]
Abstract
A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant's PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.
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14
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Wan Z, Zhang H, Chen J, Zhou H, Yang J, Zhong N. WaaS architecture-driven depressive mood status quantitative analysis based on forehead EEG and self-rating tool. Brain Inform 2018; 5:15. [PMID: 30515600 PMCID: PMC6429167 DOI: 10.1186/s40708-018-0093-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 11/08/2018] [Indexed: 11/25/2022] Open
Abstract
Background Although the objective depression evaluation is a hot topic in recent years, less is known concerning developing a pervasive and objective approach for quantitatively evaluating depression. Driven by the Wisdom as a Service architecture, a quantitative analysis method for rating depressive mood status based on forehead electroencephalograph (EEG) and an electronic diary log application named quantitative log for mental state (Q-Log) is proposed. A regression method based on random forest algorithm is adopted to train the quantitative model, where independent variables are forehead EEG features and the dependent variables are the first principal component (FPC) values of the Q-Log. Results The Leave-One-Participant-Out Cross-Validation is adopted to estimate the performance of the quantitative model, and the result shows that the model outcomes have a moderate uphill relationship (the average coefficient equals 0.6556 and the P value less than 0.01) with the FPC values of the Q-Log. Furthermore, an exemplary application of knowledge sharing, which is developed by using ontology technology and Jena inference subsystem, is given to illustrate the preliminary work for annotating data and facilitating clinical users to understand the meaning of the quantitative analysis results. Conclusions This method combining physiological sensor data with psychological self-rating data could provide new insights into the pervasive and objective depression evaluation processes in daily life.
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Affiliation(s)
- Zhijiang Wan
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 3710864, Japan
| | - Hao Zhang
- College of Economics and Management, Nanjing Forestry University, Nanjing, 210037, China
| | - Jianhui Chen
- College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China.,International WIC Institute, Beijing University of Technology, Beijing, 100088, China
| | - Haiyan Zhou
- College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China.,International WIC Institute, Beijing University of Technology, Beijing, 100088, China
| | - Jie Yang
- Beijing Anding Hospital of Capital Medical University, Beijing, 100088, China
| | - Ning Zhong
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 3710864, Japan. .,College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China. .,International WIC Institute, Beijing University of Technology, Beijing, 100088, China.
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Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR Mhealth Uhealth 2018; 6:e165. [PMID: 30104184 PMCID: PMC6111148 DOI: 10.2196/mhealth.9691] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 05/13/2018] [Accepted: 06/18/2018] [Indexed: 12/14/2022] Open
Abstract
Background Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published. Objective The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies. Methods We conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017. Results A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity). Conclusions Several studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.
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Affiliation(s)
- Darius A Rohani
- Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Center for Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Centre, Psychiatric Centre Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Centre, Psychiatric Centre Copenhagen, Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob E Bardram
- Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Center for Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
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Do We Understand the Relationship between Affective Computing, Emotion and Context-Awareness? MACHINES 2017. [DOI: 10.3390/machines5030016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Liao SC, Wu CT, Huang HC, Cheng WT, Liu YH. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns. SENSORS (BASEL, SWITZERLAND) 2017; 17:E1385. [PMID: 28613237 PMCID: PMC5492453 DOI: 10.3390/s17061385] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Revised: 06/07/2017] [Accepted: 06/10/2017] [Indexed: 01/19/2023]
Abstract
Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.
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Affiliation(s)
- Shih-Cheng Liao
- Department of Psychiatry, National Taiwan University Hospital, Taipei 10051, Taiwan.
| | - Chien-Te Wu
- Department of Psychiatry, National Taiwan University Hospital, Taipei 10051, Taiwan.
- School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei 10051, Taiwan.
| | - Hao-Chuan Huang
- Graduate Institute of Mechatronics Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
| | - Wei-Teng Cheng
- Department of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, Taiwan.
| | - Yi-Hung Liu
- Graduate Institute of Mechatronics Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
- Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
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