Correlation between electroencephalographic markers in the healthy brain

Päeske, Laura; Uudeberg, Tuuli; Hinrikus, Hiie; Lass, Jaanus; Bachmann, Maie

doi:10.1038/s41598-023-33364-z

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…The correlation between the scores of two different questionnaires supports their reliability. The character of the correlation between various EEG markers (Table 2 ) is consistent with the results of a previous study performed in a much larger sample size (n = 80) 42 . The correlation is evident between the ABP and next to alpha band theta and beta band markers TBP and BBP.…”

Section: Discussionsupporting

confidence: 89%

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Põld,

Päeske,

Hinrikus

et al. 2023

Sci Rep

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Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires’ scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.

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Section: Discussionsupporting

confidence: 89%

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Põld,

Päeske,

Hinrikus

et al. 2023

Sci Rep

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“…Thus, we observe that ricing correlations in electrical activity in the anterior regions of the cortex during rest conditions with closed eyes could reflect the diminishing inhibitory cortico-fugal descending influences from the cortex to subcortical strictures. Under such circumstances, the cortex is less active, more passively follows the rhythms produced by subcortical structures, and expresses more synchronicity in electrical activity [10]. At the same time, we reviled that synchronicity between the central and occipital brain areas which is responsible for the sensory visual perception [11] during restring state with opened eyes dropped in compere with similar condition before massage.…”

Section: Resultsmentioning

confidence: 73%

“…Previously, all EEG records were cleaned from artifacts semiautomatically (with the help of EEG device software). The cross-correlation coefficient was calculated as the measure of synchronicity of electrical activity in different brain regions [10] automatically with the help of EEG device software. The cross-correlation coefficient was calculated for each state: background rest EEG with closed eyes and with opened eyes before massage session EEG with closed (3 minutes) and opened (3 minutes) eyes after the massage.…”

Section: Eeg Record and Massage Procedures And Eeg Analysismentioning

confidence: 99%

“…When massaging the cervical-collar area, tension in the cervical and shoulder muscles is relieved, and pain in this area disappears. Massage serves as an effective means for osteochondrosis and neuralgia prevention and treatment [5], for improving blood circulation, venous and lymphatic outflow, relieving neck muscle spasms, normalizing sleep, improving emotional state, and helping cope with migraines and dizziness [6][7][8][9][10]. However, there are a lot of different types of massages with different physiological effects, and our knowledge remains pure regarding the biological mechanisms of massage benefits and objective biomarkers of its efficiency [7].…”

Section: Introductionmentioning

confidence: 99%

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Influence of neck and back massage on the synchronicity of electrical brain activity

Kulbayeva,

Shvetsova,

Abdreshov

et al. 2024

BIO Web Conf.

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Due to busy working or learning schedules, people spend a lot of time in a sitting, motionless position, which could cause tension neck syndrome and promote other harmful consequences. Massage has been used for health support for many decades. However, there are a lot of different types of massages with different physiological effects, and our knowledge remains pure regarding the biological mechanisms of massage benefits. The question of the influence of massage on functional brain state attracts scientific interest as massage is an available and attractive health-supporting tool. Particular types of massage had been found to reduce frontal brain activity, but others were associated with the activation of certain brain regions. In our study, we aimed to evaluate the impact of combined general and point massage of the cervical-collar area on the electrical activity of the brain in terms of its synchronicity in healthy young subjects. Through cross-correlation analysis of EEG before and after massage, we found that massage of the cervical-collar area increased the synchronicity of electrical activity between the central and frontal regions during resting state with closed eyes and decreased the synchronicity between the central and occipital brain areas during resting state with opened eyes, which allows us to suggest that this type of massage optimizes functional brain state.

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“…Over and above that, EEG data has the potential to be used for personalized solutions. EEG signals can be used to recognize distinctive patterns or markers coupled with different subtypes of depression or treatment responses [ 27 ]. This can help in adapting treatments to individual patients, leading to more efficient and personalized intrusions.…”

Section: Introductionmentioning

confidence: 99%

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

Khan,

Umar Saeed,

Frnda

et al. 2024

PLoS ONE

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Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.

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Correlation between electroencephalographic markers in the healthy brain

Cited by 6 publications

References 42 publications

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Influence of neck and back massage on the synchronicity of electrical brain activity

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

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