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Bailey NW, Fulcher BD, Caldwell B, Hill AT, Fitzgibbon B, van Dijk H, Fitzgerald PB. Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction. Neural Netw 2024; 171:171-185. [PMID: 38091761 DOI: 10.1016/j.neunet.2023.12.007] [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: 06/24/2023] [Revised: 11/02/2023] [Accepted: 12/04/2023] [Indexed: 01/29/2024]
Abstract
Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67 %, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators' brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.
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Affiliation(s)
- Neil W Bailey
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia.
| | - Ben D Fulcher
- School of Physics, University of Sydney, Camperdown, NSW, Australia
| | - Bridget Caldwell
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Victoria, Australia
| | - Bernadette Fitzgibbon
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia
| | - Hanneke van Dijk
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Kingdom of the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, University Maastricht, Maastricht, the Kingdom of the Netherlands
| | - Paul B Fitzgerald
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia
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Shang B, Duan F, Fu R, Gao J, Sik H, Meng X, Chang C. EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning. Front Hum Neurosci 2023; 17:1033420. [PMID: 37719770 PMCID: PMC10500069 DOI: 10.3389/fnhum.2023.1033420] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 06/16/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP). Methods We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario. Results For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively. Conclusion Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.
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Affiliation(s)
- Baoxiang Shang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Feiyan Duan
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
- Deepbay Innovation Technology Corporation Ltd., Shenzhen, China
| | - Ruiqi Fu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Junling Gao
- Buddhist Practice and Counselling Science Lab, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Hinhung Sik
- Buddhist Practice and Counselling Science Lab, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Chunqi Chang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
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Chu H, Moon S, Park J, Bak S, Ko Y, Youn BY. The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Front Pharmacol 2022; 13:826044. [PMID: 35431917 PMCID: PMC9011141 DOI: 10.3389/fphar.2022.826044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 01/04/2023] Open
Abstract
Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status. Objective: To categorize and seek the current usage of AI in CAM. Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies. Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.
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Affiliation(s)
- Hongmin Chu
- Daecheong Public Health Subcenter, Incheon, South Korea
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Jeongsu Park
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Seongjun Bak
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Youme Ko
- National Institute for Korean Medicine Development (NIKOM), Seoul, South Korea
| | - Bo-Young Youn
- Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Bo-Young Youn,
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Kora P, Meenakshi K, Swaraja K, Rajani A, Raju MS. EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review. Complement Ther Clin Pract 2021; 43:101329. [PMID: 33618287 DOI: 10.1016/j.ctcp.2021.101329] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/19/2021] [Accepted: 02/02/2021] [Indexed: 01/20/2023]
Abstract
OBJECTIVES The present investigation is to study the impact of yoga and meditation on Brain waves concerning physical and mental health. There are mainly three stages (steps) in the brain wave classification:(i) preprocessing, ii) feature extraction, and iii) classification. This work provides a review of interpretation methods of Brain signals (Electroencephalogram (EEG)) EEG during yoga and meditation. Past research has revealed significant mental and physical advantages with yoga and meditation. METHODS The research topic reviewed focused on the machine learning strategies applied for the interpretation of brain waves. In addressing the research questions highlighted earlier in the general introduction, we conducted a systematic search of articles from targeted scientific and journal online databases that included PubMed, Web of Science, IEEE Xplore Digital Library (IEEE), and Arxiv databases based on their relevance to the research questions and domain topic. The survey topic is relatively nascent, and therefore, the scope of the search period was limited to the 20-year timeline that was deemed representative of the research topic under investigation. The literature search was based on the keywords "EEG", "yoga*" and "meditation*". The key phrases were concatenated using Boolean expressions and applied to search through the selected online databases yielding a total of 120 articles. The online databases were selected based on the relevancy of content with the research title, research questions, and the domain application. The literature review search, process, and classification were carefully conducted guided by two defined measures; 1.) Inclusion criteria; and 2.) Exclusion criteria. These measures define the criteria for searching and extracting relevant articles relating to the research title and domain of interest. RESULTS Our literature search and review indicate a broad spectrum of neural mechanics under a variety of meditation styles have been investigated. A detailed analysis of various mental states using Zen, CHAN, mindfulness, TM, Rajayoga, Kundalini, Yoga, and other meditation styles have been described by means of EEG bands. Classification of mental states using KNN, SVM, Random forest, Fuzzy logic, neural networks, Convolutional Neural Networks has been described. Superior research is still required to classify the EEG signatures corresponding to different mental states. CONCLUSIONS Yoga practice may be an effective adjunctive treatment for a clinical and aging population. Advanced research can examine the effects of specific branches of yoga on a designated clinical grouping. Yoga and meditation increased overall healthy brain activity.
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Hosseini MP, Hosseini A, Ahi K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev Biomed Eng 2021; 14:204-218. [PMID: 32011262 DOI: 10.1109/rbme.2020.2969915] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of classifiers available to use, the analysis methods are also equally numerous. In this review, we will be examining specifically machine learning methods that have been developed for EEG analysis with bioengineering applications. We reviewed literature from 1988 to 2018 to capture previous and current classification methods for EEG in multiple applications. From this information, we are able to determine the overall effectiveness of each machine learning method as well as the key characteristics. We have found that all the primary methods used in machine learning have been applied in some form in EEG classification. This ranges from Naive-Bayes to Decision Tree/Random Forest, to Support Vector Machine (SVM). Supervised learning methods are on average of higher accuracy than their unsupervised counterparts. This includes SVM and KNN. While each of the methods individually is limited in their accuracy in their respective applications, there is hope that the combination of methods when implemented properly has a higher overall classification accuracy. This paper provides a comprehensive overview of Machine Learning applications used in EEG analysis. It also gives an overview of each of the methods and general applications that each is best suited to.
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Deolindo CS, Ribeiro MW, Aratanha MA, Afonso RF, Irrmischer M, Kozasa EH. A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram. Front Syst Neurosci 2020; 14:53. [PMID: 32848645 PMCID: PMC7427581 DOI: 10.3389/fnsys.2020.00053] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 07/06/2020] [Indexed: 11/13/2022] Open
Abstract
Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.
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Affiliation(s)
| | | | | | | | - Mona Irrmischer
- Department of Integrative Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU Amsterdam, Amsterdam, Netherlands
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Devipriya A, Nagarajan N. A Novel Method of Segmentation and Classification for Meditation in Health Care Systems. J Med Syst 2018; 42:193. [PMID: 30206721 DOI: 10.1007/s10916-018-1062-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 09/06/2018] [Indexed: 11/27/2022]
Abstract
Meditation improves positivity in behavioral as well as psychological changes, which are brought elucidated by knowing neuro-physiological consequences of meditation. In the field of cognitive science, neuroscience and physiological research, Electroencephalogram (EEG) is extensively utilized. The primary tasks of EEG signal analysis is to identify the noisy signal as well as enormous data that create signal processing and subsequent analysis. Beforehand any analysis of the EEG signal, the obtained raw signal must be preprocessed for eliminating undesirable artifacts as well as horrible noise. With the aim of resolving this issue, in this research, raw signals are preprocessed with the help of Band-Pass Filter (BPF) for noise removal method. Instead, in adaptive Sliding Window with Fuzzy C Means Clustering (SW-FCM) segmentation is presented, which precisely as well as automatically segments the signals. So as to analyze the accuracy, five features such as electroencephalography alpha spectrum, frequency of the main peak, Amplitude of the main peak, Higher Order Crossing (HOC), and wavelet features are used as the evaluating variables. Lastly to assess the meditation experience with Fuzzy Kernel least square Support Vector Machine (FKLSSVM) classifier, the presented method with a cross-sectional analysis is utilized. These two classifiers are utilized for meditation experience classification by utilizing an individual feature vector values from equivalent EEG signals. The dataset samples are gathered from Open source Brain-Computer Interface (Open BCI) platform. Outcomes attained are matched up for diverse techniques for identifying as well as for classifying signal segments features using MATLAB. Presented classifiers of the meditation process validate quick interpretation methods that differentiate meditation experience and valuable performance related to artificial approaches for the big-data analysis.
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Affiliation(s)
- A Devipriya
- Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, India.
| | - N Nagarajan
- Coimbatore Institute of Engineering and Technology, Narasipuram, India
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