Association between resting-state EEG oscillation and psychometric properties in perimenopausal women

Diversity and limitations of electroencephalogram and event-related potential applications in nursing research: A scoping review

AIMS

This scoping review was conducted to provide a comprehensive summary of how electroencephalogram (EEG) and event-related potentials (ERPs) have been used in nursing research, with the goal of mapping the themes and methods of nursing research involving EEGs or ERPs as a measurement tool.

METHODS

The eligibility criteria were determined according to the Population, Concept, and Context principle. A systematic electronic search of articles in the PubMed, Web of Science, Embase, CINAHL, APA PsycInfo, and Scopus databases was carried out for the period from database establishment to November 21, 2022. The included studies were analyzed using descriptive statistics and content analysis.

RESULTS

The review process culminated in 45 articles, evidencing an increasing trend and dispersion characteristics of EEG in nursing research and reflecting five thematic domains of inquiry related to nursing. There was a deficiency of detailed reports of EEG recording and data analysis parameters in nursing research. The common EEG bands in nursing research were Delta, Theta, Alpha, Beta, Gamma. The ERP components used frequently were P3, P2, N1, N2, P1, N170, and feedback-related negativity.

CONCLUSIONS

The wide variety of EEG components used show broad potential for studying nursing questions. In the future, it will be necessary to increase the depth of the research content, the repeatability of the experiment and the standardization of the report. Nursing researchers should give full play to the characteristics of nursing and establish a systematic and complete EEG research system for nursing.

Dorsal brain activity reflects the severity of menopausal symptoms

OBJECTIVE

The severity of menopausal symptoms, despite being triggered by hormonal imbalance, does not directly correspond to hormone levels in the blood; thus, the level of unpleasantness is assessed using subjective questionnaires in clinical practice. To provide better treatments, alternative objective assessments have been anticipated to support medical interviews and subjective assessments. This study aimed to develop a new objective measurement for assessing unpleasantness.

METHODS

Fourteen participants with menopausal symptoms and two age-matched participants who visited our outpatient section were enrolled. Resting-state brain activity was measured using magnetoencephalography. The level of unpleasantness of menopausal symptoms was measured using the Kupperman Kohnenki Shogai Index. The blood level of follicle-stimulating hormone and luteinizing hormone were also measured. Correlation analyses were performed between the oscillatory power of brain activity, index score, and hormone levels.

RESULTS

The level of unpleasantness of menopausal symptoms was positively correlated with high-frequency oscillatory powers in the parietal and bordering cortices (alpha; P = 0.016, beta; P = 0.015, low gamma; P = 0.010). The follicle-stimulating hormone blood level was correlated with high-frequency oscillatory powers in the dorsal part of the cortex (beta; P = 0.008, beta; P = 0.005, low gamma; P = 0.017), whereas luteinizing hormone blood level was not correlated.

CONCLUSION

Resting-state brain activity can serve as an objective measurement of unpleasantness associated with menopausal symptoms, which aids the selection of appropriate treatment and monitors its outcome.

Impulsivity Classification Using EEG Power and Explainable Machine Learning

Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified [Formula: see text]-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power ([Formula: see text]-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model's output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.