Effect of meditation on emotional response: An EEG-based study

Jadhav, Narendra; Manthalkar, Ramchandra; Joshi, Yashwant

doi:10.1016/j.bspc.2017.01.008

Cited by 53 publications

(16 citation statements)

References 30 publications

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“…Another group reported the feasibility of using asymmetry in frontal EEG band power (especially the Alpha band) as a metric for user engagement while reading the news [45]. Band power asymmetry was also introduced as a study feature on the effects of meditation on emotional responses [46]. Some groups explored simultaneous EEG recordings from multiple subjects such as the research on synchronized-EEGs while students were studying in the same classroom [47] and similar research on the emotional engagement between a performer and multiple audiences [48].…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

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Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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“…Such electrophysiological findings have also been coupled with positive effects on cognitive performance and affective responses (cf. Jadhav, Manthalkar, & Joshi, ). Therefore, it appears reasonable to postulate that the brain mechanisms underlying the effects of MF meditation on exercise are possibly associated with changes in anterior–posterior connectivity of low‐ and high‐frequency components of the power spectrum.…”

Section: Introductionmentioning

confidence: 99%

Effects of mindfulness on psychological and psychophysiological responses during self‐paced walking

et al. 2020

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The aim of the present study was to investigate the effects of an audio‐guided mindfulness (MF) single session on psychological and psychophysiological responses during an outdoor walking task. Twenty‐four participants (12 females and 12 males; Mage = 23.6, SD = 3.9 years) were required to walk 200 m at a pace of their choosing. Two experimental conditions (mindfulness meditation and mindlessness [ML] meditation) and a control condition (CO) were administered. Electrical activity in the brain was measured by the use of a portable electroencephalography (EEG) system during walking. Fast Fourier Transform was used to decompose the EEG samples into theta (5–7 Hz), alpha (8–14 Hz), and beta (15–29 Hz) frequencies. Brain connectivity analysis between frontal and temporo‐parietal electrode sites was conducted to explore functional interactions through the use of spectral coherence. Affective and perceptual responses were measured by the use of single‐item scales and questionnaires. The present findings indicate that MF was sufficiently potent to reallocate attention toward task‐related thoughts, downregulate perceived activation, and enhance affective responses to a greater degree than the other two conditions. Conversely, ML was sufficient to increase the use of dissociative thoughts, make participants less aware of their physical sensations and emotions, induce a more negative affective state, and upregulate perceived activation to a greater extent than MF and CO. The brain mechanisms that underlie the effects of MF on exercise appear to be associated with the enhanced inter‐hemispheric connectivity of high‐frequency waves between right frontal and left temporo‐parietal areas of the cortex.

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“…This work is significant to those past studies in which only second order measures of statistics, such as the power spectrum [ 50 , 51 ], which is a linear feature, was used. The power spectrum can only reveal the amplitude information about the EEG signals, the phase information, such as the phase coupling in the signal, cannot be observed by applying the power spectrum.…”

Section: Resultsmentioning

confidence: 96%

An Emotion Assessment of Stroke Patients by Using Bispectrum Features of EEG Signals

et al. 2020

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Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.

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Effect of meditation on emotional response: An EEG-based study

Cited by 53 publications

References 30 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

Effects of mindfulness on psychological and psychophysiological responses during self‐paced walking

An Emotion Assessment of Stroke Patients by Using Bispectrum Features of EEG Signals

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