EEG-Based Brain-Electric Activity Detection During Meditation Using Spectral Estimation Techniques

Kora, Padmavathi; Rajani, A.; Chinnaiah, M. C.; Madhavi, K. Reddy; Swaraja, K.; Meenakshi, K.

doi:10.1007/978-981-16-1941-0_68

Cited by 15 publications

(3 citation statements)

References 11 publications

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“…It occupies a very little volume of the structure in installation and is very applicable for multiband wireless communications [11,12] along with image processing applications [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

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confidence: 99%

Sustainable Design of Multiband Antenna for Wireless Communication Applications

Aouthu,

Anjaiah,

Gireesh

et al. 2023

E3S Web Conf.

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This work emphasises sustainability as a crucial component of the design process and offers a novel approach to antenna design. A miniature circular microstrip antenna array with 1800 hybrid coupler operating at different modes of frequency, specifically aiming for multiband wireless communication is presented. The Circular patch, two element broad side array and end fire array structures are designed and analysed by using High Frequency Software Simulator (HFSS) software. Measured results are also presented for validating the simulated results.

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confidence: 99%

Sustainable Design of Multiband Antenna for Wireless Communication Applications

Aouthu,

Anjaiah,

Gireesh

et al. 2023

E3S Web Conf.

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“…The study aids in developing proactive diabetes management strategies by leveraging ensemble techniques within smart health contexts [12]. Various other techniques involving image processing and machine learning techniques [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] were also taken into consideration for the proposed work.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Comparison of ML Algorithms for Sustainable Smart Health Systems

Sujatha,

Swaraja,

Bharath Kumar

et al. 2023

E3S Web Conf.

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Disease prognosis holds immense significance in healthcare due to its potential to greatly improve patient outcomes through early and precise diagnosis. Machine learning (ML) algorithms provide a robust avenue for disease prediction, employing patient data analysis to detect intricate patterns of specific ailments. Machine learning algorithms adeptly handle intricate and extensive datasets, uncovering latent patterns often eluding human observation. By considering diverse symptoms and their permutations, ML models yield precise forecasts concerning the probability of distinct diseases. The investigation begins by laying a basis in sustainable development concepts, recognising the need of resource optimisation, energy efficiency, and minimal environmental effect in the context of healthcare technology. Categorically, disease prediction methodologies fall under supervised and unsupervised learning categories, involving training algorithms on annotated datasets containing symptoms and corresponding diagnoses. These trained models can then anticipate diseases based on novel symptom profiles.

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“…Low-frequency alpha power reflects internal attention or non-task-related cognitive activities (25) . EEG studies have revealed a significant increase in alpha and theta activity during meditation (26) . Our study results also fit with previous study results.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Effect of Simplified Kundalini Yoga Meditation on Engineering Students: An EEG Study

Venkatesan¹,

Ilavarasu²

2022

IJST

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Objectives: Meditation influences brain waves. The purpose of the present study was to investigate the effect of Simplified Kundalini Yoga (SKY) meditation on the brain waves of technical college students using the Neurosky mindwave Electroencephalogram (EEG) device. Methods: Eighty-six participants were recruited from an engineering college in South India, with ages ranging from 18 to 19 years. They were divided into the yoga group (43 participants) and the sports group (43 participants). During pre-data collection, students of both groups were asked to sit with closed eyes for 20 minutes. During post-data collection, students in the sports group sat with closed eyes for 20 minutes, and yoga group students performed Thuriya meditation for 20 minutes. Findings: Pre-test and post-test results showed significant differences within sessions in EEG meditation state and Low Alpha state in both groups. After the intervention, the sports group showed a significant difference at the end of the session, whereas the yoga group showed a significant difference at the beginning of the session. At the baseline, there were significant differences between groups in the High Gamma state and within sessions in the Mid Gamma state. In terms of other variables, there was no significant difference in both groups. Novelty: We conclude that the SKY meditation technique can influence the state of mediation, low alpha, high gamma, and mid gamma EEG bands. Sports group have also shown comparable improvements. Hence, meditation and sports activities can be suggested in technical educational institutes for the promotion of positive health.

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EEG-Based Brain-Electric Activity Detection During Meditation Using Spectral Estimation Techniques

Cited by 15 publications

References 11 publications

Sustainable Design of Multiband Antenna for Wireless Communication Applications

Sustainable Design of Multiband Antenna for Wireless Communication Applications

Performance Comparison of ML Algorithms for Sustainable Smart Health Systems

Effect of Simplified Kundalini Yoga Meditation on Engineering Students: An EEG Study

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