Real-time Sensing and NeuroFeedback for Practicing Meditation Using simultaneous EEG and Eye Tracking

Pandey, Pankaj; Gupta, Pragati; Chaudhary, Shivam; Miyapuram, Krishna Prasad; Lomas, Derek

doi:10.1109/tensymp54529.2022.9864414

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…The continued use of machine learning methods, for example, can be used to improve the quality of EEG and fNIRS data by filtering out noise and discovering patterns related to mental health. Newer EEG devices are also incorporating additional sensors, such as eye tracking [5] or heart rate monitors, to provide more comprehensive data. There are efforts currently underway to standardize data collection and analysis techniques, which will make it easier to compare results across research studies and create the best practices for clinical application.…”

Section: Challengesmentioning

confidence: 99%

“…State-of-the-art technology is beginning to address some of these challenges. For example, machine learning algorithms can be used to improve the quality of deciphering EEG and fNIRS signals by filtering out noise and identifying patterns that are relevant to mental health and wellbeing monitoring [4,5]. Newer EEG devices are also incorporating additional sensors, such as eye-tracking or heart rate monitors, to provide more comprehensive data and an inertial measurement unit (IMU) to measure and correct motion artifacts.…”

Section: Introductionmentioning

confidence: 99%

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Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

Flanagan,

Saikia

2023

Sensors

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Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks.

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Section: Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

Flanagan,

Saikia

2023

Sensors

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“…Machine learning classifiers are trained as the most practical method for spotting differences due to their strong pattern learning capabilities. Moreover, there has been a surge in studies using machine learning to categorize meditation states in recent years (Chaudhary et al, 2022;Pandey et al, 2022;Pandey & Miyapuram, 2020;Pandey & Miyapuram, 2021a, 2021c. Note.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Brain Network Measures Across Three Meditation Traditions

Pandey

Gupta²,

Miyapuram³

2022

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Research into the similarities and differences between various forms of meditation practice is still in its early stages. Here, utilizing functional connectivity and graph measures, we present our work examining three meditation traditions: Himalayan Yoga (HT), Isha Shoonya (SNY), and Vipassana (VIP). EEG activity of the meditative block is used to build functional brain connections to exploit the resulting networks between various meditation traditions and a control group. Support vector machine is employed for binary classification, and models are built with features generated via graph theory measures. We obtain maximum accuracy of 84.76% with gamma1, 90% with alpha, and 84.76% with theta in HT, SNY, and VIP, respectively. Our key findings involve (a) higher delta connectivity in Vipassana meditators, (b) synchronization of theta networks in the left hemisphere inspected to be stronger in the anterior frontal area across meditators, (c) greater involvement of gamma2 processing observed among Himalayan and Vipassana meditators, (d) increased left frontal activity contribution for all meditators in theta and gamma bands, and (e) modularity engaged extensively in gamma processing across all meditation traditions. Furthermore, we discuss the implication of this research for neurotechnology products to enable guided meditation among naive practitioners.

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Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation

Singh

Pandey

Chaudhary

et al. 2022

Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing

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Real-time Sensing and NeuroFeedback for Practicing Meditation Using simultaneous EEG and Eye Tracking

Cited by 4 publications

References 35 publications

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

Exploration of Brain Network Measures Across Three Meditation Traditions

Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation

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