Meditation eeg dataset. 2 Deep Learning with EEG Signals.

Meditation eeg dataset Jun 17, 2019 · Aim: This dataset aims to provide open access of raw EEG signal to the general public. We believe that such fusion of human moods (Relaxation & concentration) shall increase scientific transparency and efficiency, promote the validation of published methods, and foster the development of new algorithms. A debate on the EEG changes during meditation, controversial adverse effects of meditation, and signal processing challenges with future direction has been given below. 7 %µµµµ 1 0 obj >/Metadata 1913 0 R/ViewerPreferences 1914 0 R>> endobj 2 0 obj > endobj 3 0 obj >/ExtGState >/ProcSet[/PDF/Text/ImageB/ImageC/ImageI A method for detecting α wave in EEG (electroencephalograph) is proposed and the characteristics of EEG spatial distribution are found and activating medial prefrontal cortex and anterior cingulated cortex during meditation may be the reason of increasing frontal α power. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. Learn more Data collection took place at the Meditation Research Institute (MRI) in Rishikesh, India under the supervision of Arnaud Delorme, PhD. The EEG recording sessions were conducted at three intervals: the first day (baseline), the end of the first week, and the end of the second week, as depicted in Fig. Participants: 36 of them were diagnosed with Alzheimer's disease (AD group), 23 were diagnosed with Frontotemporal Dementia (FTD group) and 29 were healthy subjects (CN group). We conduct our research on two different types of meditation - Himalayan Yoga (HT) and Hare Krishna mantra meditation (HKT). From the raw EEG data, power spectral density using Welch's method, absolute power was calculated for each α,β,γ,δ,θ bands. The behavioral data contain participant characteristics, while the EEG data provide absolute and relative powers of five frequency bands (delta, theta, alpha, beta, and gamma) during the 30-minute meditative states of the 60 Thai monks. Multi-channel EEG Sep 9, 2023 · 4. - Arnaud Delorme (October 17, 2018) ©2024 上海长数新智科技有限公司 版权所有 沪icp备2024081699号-1 This repository is the official page of the CAUEEG dataset presented in "Deep learning-based EEG analysis to classify mild cognitive impairment for early detection of dementia: algorithms and benchmarks" from the CNIR (CAU NeuroImaging Research) team. The headband houses seven electrodes that sits on the forehead and behind the ears. starting session where EEG data are collected before . They commonly compare frequency sub-band powers for analyzing the inter-group or inter-state differences with the help Feb 8, 2025 · This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. HKT is an in-house study of a group of 16 experienced meditators who participated in a two-week mantra meditation practice. We present the Chinese Imagined Speech Corpus (Chisco), including over 20,000 Nov 19, 2018 · This meditation experiment contains 24 subjects. The dataset comprises EEG recordings and cognitive data from 71 participants undergoing two testing sessions: one involving SD and … OpenNeuro is a free and open platform for sharing neuroimaging data. EEG neural correlates underlying enhanced cognitive abilities such as sustained attention and working memory need to be analyzed scrutinizingly to evaluate the effects of meditation practices. Experiment: EEG recordings from 15 young adults during a visual reasoning test and meditation Jul 1, 2022 · We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. All subjects underwent 7-11 sessions of BCI training which involves controlling a computer cursor to move in one-dimensional and two-dimensional spaces using subject’s “intent”. Baseline EEG data were collected from both groups. 2019). This notebook provides a step-by-step approach to preprocess the data Feb 2, 2021 · Raw data of single channel dry electrodes EEG: How data were acquired: Data were acquired using a BCI headset built on top of the Olimex EEG-SMT, a two-channel differential input 10-bit analogue-digital converter (ADC) with a sampling frequency of 256 Hz. Sep 9, 2023 · In summary, using the loving kindness meditation EEG dataset (Pre-Resting, Post-Resting, LKM Self and LKM Others) two studies were conducted using the available readable data. One channel out of two has been used for developing the proposed dataset. Apr 24, 2024 · To investigate the impact of sleep deprivation (SD) on mood, alertness, and resting-state electroencephalogram (EEG), we present an eyes-open resting-state EEG dataset. Oct 3, 2024 · Electroencephalography (EEG)-based open-access datasets are available for emotion recognition studies, where external auditory/visual stimuli are used to artificially evoke pre-defined emotions. marked against various EEG datasets, showcasing its prowess compared to Shallow Con- vNet, Deep EEGNet, FBCNet, ConvNet, ResNet and EEG TCNet (Samizade and Abad, 2018). EEG rhythms show six times less power in 25–30 Hz band and 100 times less 40–100 Hz power in paralyzed subjects [113]. Dec 15, 2021 · Skin abrasion and electrode paste (Nuprep) were used to reduce the electrode impedances during the recordings. It has been reported that the amplitude of electroencephalographic (EEG) infra-slow activity (ISA, < 0. Thirty-six input datasets (3 indices of the spectrum × 2 baseline conditions × 6 channels) and 3 output categories (Ŝ, Ĵ, and οN) were set in ANN, whereas the data were normalized in a Oct 9, 2022 · In addition, a novel dataset with the name EEG eye state, for benchmarking learning methods, is presented. This study supports previous findings that short-term meditation training has EEG … Mar 15, 2024 · A large share of the existing EEG-based studies [2, 4, 5, 31] in meditation research focus only on a statistical analysis of EEG correlates of meditators, in an attempt to find significant state and trait effects of meditation. 2%, which is much inferior to the resting state EEG based stage # General information The dataset provides resting-state EEG data (eyes open,partially eyes closed) from 71 participants who underwent two experiments involving normal sleep (NS---session1) and sleep deprivation(SD---session2) . The code of this repository was developed in Python 3. A detailed quantitative analysis of neural effects under the effect of various meditation states has been discussed below. Sep 13, 2022 · Here we present a test-retest dataset of electroencephalogram (EEG) acquired at two resting (eyes open and eyes closed) and three subject-driven cognitive states (memory, music, subtraction) with Sep 9, 2023 · 4. The exploration expands with Adeli and Ghosh-Dastidar (2010), outlining a wavelet-chaos The BBIT was also administered to evaluate their cognitive abilities before and after a two-week meditation practice. load_labels() Loads labels from the dataset and transforms the Feb 12, 2024 · Typically EEG can be categorised into five main frequency bands: Delta band (0–4Hz, generally present in certain states of deep sleep, meditation or deep relaxation), Theta band (4–8Hz, generally present in states of relaxation, daydreaming, and light sleep, including the early stages of the sleep cycle), Alpha band (8–13Hz, most My primary research interest is in the analysis and modeling of human consciousness as captured by high-dimensional EEG, MEG, and other imaging modalities. I will keep updating it with latest research and resources. This study proposes an approach to classify the EEG into meditation and non Nov 21, 2024 · The absence of imagined speech electroencephalography (EEG) datasets has constrained further research in this field. 1 Understanding the EEG meditation dataset based . Consequently, we aimed to determine if EEG ISA amplitude decreases as a result of meditation practice across various traditions. Feb 20, 2024 · This dataset comprises EEG and behavioral data recorded from 60 Thai Buddhist monks who voluntarily participated in the research project. In addition to the EEG data, behavioral data Jul 9, 2017 · This paper presents the study to detect “meditation” brain state by analyzing electroencephalographic (EEG) data, and found that overall Sample entropy is a good tool to extract information from EEG data. In addition to the EEG data They are EEG data recorded in the pre-meditation learning phase, the meditation phase, and the post-meditation learning phase, respectively. May 14, 2017 · 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 . This might be specially 111 relevant in the case of drowsiness, as it has been shown to be highly correlated to mind 112 wandering occurrence during meditation (Brandmeyer & Delorme, 2018). 8-year meditation experience and 15 ordinary, healthy volunteers (control group). Aug 2, 2019 · 3. Please cite the following publication for using the codes and dataset. EEG is record of the electrical activity of the brain from the scalp. In this project, resting EEG Oct 27, 2020 · Results suggested the meditation intervention had large varying effects on EEG spectra (up to 50 % increase and 24 % decrease), and the speed of change from pre-meditation to post-meditation state of the EEG co-spectra was significant (with 0. Sep 13, 2022 · Here we present a test-retest dataset of electroencephalogram (EEG) acquired at two resting (eyes open and eyes closed) and three subject-driven cognitive states (memory, music, subtraction) with The most convenient method for characterizing meditation state is EEG, which is the physiological electrical activity of especially when only a small dataset is available. In summary, using the loving kindness meditation EEG dataset (Pre-Resting, Post-Resting, LKM Self and LKM Others) two studies were conducted using the available readable data. Possible values are raw, wt_filtered, ica_filtered. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. 1 Dataset and Models. Since confusion is a dynamic process, an EEG-based recognition system can help educators quantify and monitor the students' cognitive state (which spans into attention, meditation, concentration Apr 7, 2023 · EEG datasets generated with Muse technology—some of the largest in the world—have enabled the application of a new machine learning approach. The EEG signal was amplified using a unipolar amplifier with a sampling rate of 512 Hz. Extract those 16 channels data from each data file and save them in a data frame, hence 3 data frames. Using EEG (electroencephalogram) signals, the Oct 1, 2015 · Furthermore, EEG analysis of meditation may be affected by whether the control task is a resting state or a cognitive task, as increased theta amplitude during meditation has been observed in comparison to a resting state baseline, but was comparable in amplitude to an executive attention task, with these patterns further modulated by the Ear-EEG Meditation Spectral & Statistical Analysis Repository with basic scripts for using the Ear-EEG Dataset developed at NextSense. The prime objective of the study is to investigate the effect (effects in the sense of an increase in psychological well-being and decrease in stress & mood disturbances) of specific relaxation technique popularly named as Kriya Yoga (KY) meditation on long-term and short-term practitioners. Works with all popular EEG headsets, providing adaptive feedback for any kind of meditation and mental activity. The physiological signals during meditation and control Apr 24, 2024 · To investigate the impact of sleep deprivation (SD) on mood, alertness, and resting-state electroencephalogram (EEG), we present an eyes-open resting-state EEG dataset. The EEG data were recorded through 6 protocols and 11 tasks. We compare performance with six commonly used machine learning classifiers and four Electroencephalography(EEG) dataset during Naturalistic Music Listening comprising different Genres with Familiarity and Enjoyment Ratings KP Miyapuram, N Ahmad, P Pandey, D Lomas Data in brief , 2022 Feb 1, 2024 · Various performance measures for each classifier are evaluated and then compared to know which classifier is effective in the classification of the EEG data into yoga, meditation, and combined May 14, 2014 · Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. The EEG offline analysis and stress healing modules are designed to be platform-independent. EEG analyses. We analyzed EEG data from a cohort of seven participants with a unique background in Vipassana meditation, in order to discern Meditation methods, which have their origins in ancient traditions are gaining popularity as a result of their potential mental and physical health advantages. In addition to the EEG data The Nencki-Symfonia EEG/ERP dataset: high-density electroencephalography (EEG) dataset obtained at the Nencki Institute of Experimental Biology from a sample of 42 healthy young adults with three cognitive tasks: (1) an extended Multi-Source Interference Task (MSIT+) with control, Simon, Flanker, and multi-source interference trials; (2) a 3 This contains all the resources related to meditation research including neuroscience, cognitive science, philosophy, computational, signal processing and machine and deep learning. We collected 12 minutes for each Jun 16, 2023 · For EEG-based classification of meditation experience using trait characteristics, in Sharma et al. The project was approved by the local MRI Indian ethical committee and the ethical committee of the University of California San Diego (IRB project # 090731). So muscle contamination is an essential issue in defining gamma EEG during meditation. Many research already conducted in order utilize deep learning with EEG signals. When there were more than 5 good-quality sessions available, the 5 sessions with the largest file sizes were selected and used in this study. The data can be used to analyze the changes in EEG signals through time (permanency). Please refer to the academic paper, "Deep May 1, 2020 · Kaggle has a dataset of an EEG conducted on a meditation group versus a control. Here, two meditation techniques, LKM-Self This dataset contains Electroencephalogram (EEG) signals recorded from a subject for more than four months everyday (some days are missing). From the on-site EEG experiments, we obtained meditation EEG recordings from 34 volunteers with varying meditation experience. In addition to the EEG data Jan 29, 2024 · The neurophysiological results are planned to be published as a separate article, while in this study, we present the SDA as a method designed specifically to process such a unique dataset and trace Guhyasamaja meditation hidden dynamics on EEG. The scientific article (see Reference file) contains all methodological details. Loads data from the SAM 40 Dataset with the test specified by test_type. Additionally, data spans different mental states like sleep, meditation, and cognitive tasks. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function Apr 19, 2023 · Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. 2. The final dataset contained about 9000 instances extracted from the 5 min non-meditation baseline and the latter 5 min of guided meditation. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The 64-channel EEG Oct 6, 2023 · EEG Data Acquisition Using the Muse Device: Meditation and Rest Stages of Participants This study aimed to investigate electroencephalogram (EEG) patterns during meditation to gain insights into the distinctions among practitioners with differing levels of experience. This means more reliable automation, which could help lower costs and increase access to insights from EEGs for Muse as well as the global neuroscience community. The primary goal of this project is to classify EEG signals into rest and task states using various machine learning models. Electroencephalography (EEG) is a non-invasive method to record electrical activity of the brain. May 12, 2021 · A set of electroencephalogram (EEG) signals data obtained from NeuroSky. This method has been repeated ten times with each subset being used for testing. Returns an ndarray with shape (120, 32, 3200). We compare performance with six commonly used machine learning classi ers and four state of the art deep learning models. 76 probability of entering end-meditation state within the first minute). For the second study, EEG data for 15 participants collected in 5 sessions were May 1, 2021 · Literature already exists on the meditation-induced changes in EEG brain waves. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting Apr 17, 2024 · Purpose Meditation is renowned for its positive effects on cognitive abilities and stress reduction. The multilayer feed-forward neural networks were used to classify the meditation experience level from the EEG responses while the subject is in meditation. The dataset was partitioned into test/train data. Meditation techniques are broadly divided into three categories. , 2013; Badran et al. This database includes the de-identified EEG data from 62 healthy individuals who participated in a brain-computer interface (BCI) study. One key component of such applications is the ability to accurately decode the state of meditation from electroencephalography (EEG) signals in real-time, with as small calibration as possible. Here, two meditation techniques, LKM-Self experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. Each Sep 24, 2024 · The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. Oct 16, 2024 · For dataset 2, data from the Meditation Research Institute in each successfully passing through the eight stages of Guhyasamaja meditation during EEG recording with the NVX-52 acquisition Feb 8, 2025 · This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. The raw EEG data was pre-processed and filtered, ICA was applied, and spectral analysis was done. 0 using the Nexus trigger interface (Mind Media). IEEE Nov 11, 2018 · The meditation has a connection with human cognition and perceptual activity which related to gamma wave [13, 14]. Jul 16, 2017 · Neuroscientific studies, particularly EEG, are revealing much about the neural correlates of meditation in the hopes of understanding why it has therapeutic value, and as a way to probe the nature of self and consciousness. 12 . Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. In this article, we thus provide an EEG brainwave data was recorded for each participant throughout the meditation session, with pre-meditation EEG data compared to end-point meditation EEG data for each session of the meditation training program. 2 Deep Learning with EEG Signals. Research shows a strong link between meditation and changes in EEG patterns, spanning various techniques. EEG was measured using a standard 10/20 19-electrode array. In addition, EEG-DaSh will incorporate a subset of the data converted from NEMAR, which includes 330 MEEG BIDS-formatted datasets, further expanding the archive with well-curated, standardized neuroelectromagnetic data. We attain comparable performance utilizing less than 4% of the parameters of other models. Expert and Non-Expert Himalayan Yoga Meditators(Meditation and Mindwandering) EEG alpha-theta dynamics during mind wandering in the context of breath focus meditation; Contrasting Electroencephalography-Derived Entropy and Neural Oscillations With Highly Skilled Meditators; Breathing, Meditating, Thinking two data files of EEG recordings, one meditation and one baseline Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. May 1, 2021 · Also, meditation effects on the brain activity measured by EEG could be contaminated by the electro muscular artifacts. 1 Hz) is reduced as the stress level decreases. In addition, publishing research data is becoming more important as public funding agencies Jan 1, 2025 · The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. Aug 31, 2023 · 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. Guided meditation with music was developed as an intervention to improve attention. Corresponding to HT, there is a publicly accessible online dataset of 16 experienced meditators. I am the originator of the widely used EEGLAB signal processing environment for MATLAB, in collaboration with Scott Makeig, and I continue to develop new tools and signal processing methods Dec 1, 2023 · This application includes EEG signal offline analysis and stress healing techniques, such as guided meditation and singing bowl sound therapy, combined with real-time EEG analysis using the Enobio-8 device. (2015) used the proposal of Lutz et al. For the second study, EEG data for 15 participants collected in 5 sessions were Feb 12, 2021 · This database includes the de-identified EEG data from 62 healthy individuals who participated in a brain-computer interface (BCI) study. The fluctuations in EEG during yoga and meditation are to be analyzed. This work investigates the problem of cross-subject mindfulness meditation decoding from EEG Aug 2, 2022 · Meditation and Schulte Grid trainings were done as interventions. , 2017). on the results obtained. In recent years, assisting meditation using neuro-feedback applications has become increasingly popular. Jul 6, 2021 · We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. EEG meditation study OpenNeuro/NEMAR Dataset: ds001787 #Files: 141 Dataset size: 5. Jan 1, 2022 · The identification of a reliable EEG correlate of attentional lapses during meditation could promote the development of EEG-neurofeedback protocols aimed at facilitating meditation practice (Brandmeyer and Delorme, 2013, 2020; Ros et al. We firstly discuss what is “meditation” state and In summary, using the loving kindness meditation EEG dataset (Pre-Resting, Post-Resting, LKM Self and LKM Others) two studies were conducted using the available readable data. The whole EEG dataset is divided into ten subsets. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. EEG during Meditation. The six protocols are baseline(2 tasks), emotional state(4 tasks), memorize task, executive task, recall task, and baseline extension(2 tasks). Base idea behind project is to fit brain pattern of mental activity on the fly (tuning phase) and then provide real-time sound feedback if required mental activity fades away (feedback Dec 1, 2020 · The machine learning model showed a high degree of accuracy for discerning pre-mediation and end-meditation EEG co-spectra for each meditation technique. The neural dynamics of each mediation technique was then assessed by applying machine learning models to the EEG co-spectra forming a classification series. In the meditation with experience sampling condition, EEG recordings were synchronized to E-prime 2. All but one subject underwent 2 sessions of BCI experiments that involved controlling a computer cursor to move in one-dimensional space using their “intent”. 2. To the best of Nov 12, 2018 · Muse’s flagship product, the Muse headband, is a consumer-grade electroencephalogram (EEG) device that provides real-time neurofeedback during meditation. There are 30 participants (female = 15, male = 15) join the data collection. The dataset and codes are freely available for research use. We evaluate the models using the leave-one-out validation technique to train on three meditative styles and test the fourth left-out style. In the first phase of this research, an existing raw EEG dataset was imported into the Python ML model (Fig. EEG signals were collected in 2002-2007 from 15 Zen-meditation practitioners (experimental group) with an average of 5. This paper presents the study we have done to detect “meditation” brain state by analyzing electroencephalographic (EEG) data. This model can be employed Dec 17, 2018 · Summary: This dataset contains electroencephalographic recordings of subjects in a simple resting-state eyes open/closed experimental protocol. Since changes in Open-source EEG neurofeedback for meditation. Data format: Raw This dataset contains the EEG resting state-closed eyes recordings from 88 subjects in total. Since we find reduced EEG complexity during mind wandering relative to breath Feb 1, 2024 · Meditation can significantly improve physical and mental relaxation (Sharma et al. Jul 7, 2021 · 110 in studies investigating the EEG correlates of meditation practice. The data_type parameter specifies which of the datasets to load. Feb 16, 2021 · 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 Aug 31, 2023 · EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning We would like to show you a description here but the site won’t allow us. (2008) (a “method definition approach” Nash and Newberg, 2013), that divides the practices into two broad categories: (i) Focused attention (FA), encompassing a pool of practices aimed at sustaining the focus on a We use EEG recording done during meditation sessions by experts of different meditative styles, namely shamatha, zazen, dzogchen, and visualization. For comparison, the EEG data for non-meditators or control group has also been recorded. Methods To this recordings of young adults. In the first study, EEG data for 32 participants involved with a single session were used. Feb 19, 2025 · Electroencephalography (EEG) is one of several methods for measuring brain activity, it is non-invasive, portable, inexpensive and time-sensitive. Contribute to OpenNeuroDatasets/ds001787 development by creating an account on GitHub. Other than that, if you are looking for the raw datasets of fmri meditation studies, that may be a little more difficult. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 [1]. Subjects were meditating and were interrupted about every 2 minutes to indicate their level of concentration and mind wandering. To get a better understanding of the brain’s activities during yoga and meditation, we have to record the EEG signal while performing the yoga and meditation practices. When considering the 4 mind tasks, Pre-Resting is the . 5). (2019) an ANN is designed to recognize combined Yoga and Sudarshan Kriya meditation experience from resting state EEG data and its mix-subject classification accuracy is 87. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. The K-NN is trained with nine subsets and the remaining subset is used for testing. The dataset comprises EEG %PDF-1. This database includes the de-identified EEG data from 37 healthy individuals who participated in a brain-computer interface (BCI) study. With machine learning playing a major role, EEG datasets have made comprehensive study possible. A new dataset with powers formed input to the ML model. Mohit Agarwal, Raghupathy Sivakumar BLINK: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). A study revealed that meditation induces electrical changes in the brain vary among five different traditions of meditation like Tibetan Buddhists (TB), Qigong Yoga, Sahaja Yoga (SY), Ananda Marga Yoga (AY), and Zen [26]. In a systematic review of mindfulness meditation and EEG findings, Lomas et al. The dataset also provides information on participants' sleepiness and mood states. EEG data were recorded with 62 electrodes. Analysis of the dataset aimed to extract effective biological markers of eye movement and EEG that can assess the concentration Apr 1, 2017 · The classification analysis result has been verified by 10-fold cross-validation method to the dataset. 7 GB #EEG Channels: 64 #Misc Channels: 15 Jun 21, 2024 · Abstract. OpenNeuro dataset - EEG meditation study.