spectral analysis
Research Papers
The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies
Whereas the link between psychosocial stress and health complications has long been established, the influence of psychosocial stress on brain activity is not yet completely understood. Electroencephalography (EEG) has been regularly employed to investigate the neural aspects of the psychosocial stress response, but these results have not yet been unified. Therefore, in this article, we systematically review the current EEG literature in which spectral analyses were employed to investigate the neural psychosocial stress response and interpret the results with regard to the three stress phases (anticipatory, reactive, and recovery) in which the response can be divided. Our results show that three EEG measures, alpha power, beta power and frontal alpha asymmetry (FAA), are commonly utilized and that alpha power consistently decreases, beta power shows a tendency to increase, and FAA varies inconsistently. We furthermore found that whereas changes in alpha power are independent of the stress phase, and changes in beta power show a relative stress phase independent trend, other EEG measures such as delta power, theta power, relative gamma and theta-alpha power ratio show less stress phase independent changes. Meta-analyses conducted on alpha power, beta power and FAA further revealed a significant effect size (hedge's g = 0.6; p = 0.001) for alpha power, but an insignificant effect size for beta power (hedge's g = -0.31; p = 0.29) and FAA (hedge's g = 0.01, p = 0.93). From our results, it can be concluded that psychosocial stress results in significant changes in some spectral EEG indices, but that more research is needed to further uncover the precise (temporal) mechanisms underlying these neural responses.
View Full Paper →EEG spectral analysis in insomnia disorder: A systematic review and meta-analysis
Insomnia disorder (ID) has become the second-most common mental disorder. Despite burgeoning evidence for increased high-frequency electroencephalography (EEG) activity and cortical hyperarousal in ID, the detailed spectral features of this disorder during wakefulness and different sleep stages remain unclear. Therefore, we adopted a meta-analytic approach to systematically assess existing evidence on EEG spectral features in ID. Hedges's g was calculated by 148 effect sizes from 24 studies involving 977 participants. Our results demonstrate that, throughout wakefulness and sleep, patients with ID exhibited increased beta band power, although such increases sometimes extended into neighboring frequency bands. Patients with ID also exhibited increased theta and gamma power during wakefulness, as well as increased alpha and sigma power during rapid eye movement (REM) sleep. In addition, ID was associated with decreased delta power and increased theta, alpha, and sigma power during NREM sleep. The EEG measures of absolute and relative power have similar sensitivity in detecting spectral features of ID during wakefulness and REM sleep; however, relative power appeared to be a more sensitive biomarker during NREM sleep. Our study is the first statistics-based review to quantify EEG power spectra across stages of sleep and wakefulness in patients with ID.
View Full Paper →Test–retest reliability of resting EEG spectra validates a statistical signature of persons
Objective When EEG is recorded in humans, the question arises whether the resting EEG remains stable. We compared the inter-individual variation in spectral observables to the intra-individual stability over more than a year. Methods We recorded resting EEG in 55 healthy adults with eyes closed. In 20 persons EEG was recorded in a second session with retest intervals 12–40 months. For electrodes AFz, Cz and Pz α peak frequency and α peak height were transformed into Z-scores. We compared the curve shape of power spectra by first aligning α peaks to 10Hz and then regressing spectra pairwise onto each other to calculate a t-value. The t-value and differences of Z-scores for all pairs of sessions were entered into a generalized linear model (GLM) where binary output represents the recognition probability. The results were cross-validated by out-of-sample testing. Results Of the 40 sessions, 35 were correctly matched. The shape of power spectra contributed most to recognition. Out of all 2960 pairwise comparisons 99.5% were correct, with sensitivity 88% and specificity 99.5%. Conclusions Our statistical apparatus allows to identify those spectral EEG observables which qualify as statistical signature of a person. Significance The effect of external factors on EEG observables can be contrasted against their normal variability over time.
View Full Paper →Changes in EEG Power Spectra During Biofeedback of Slow Cortical Potentials in Epilepsy
The goal of the study was to explore parallel changes in EEG spectral frequencies during biofeedback of slow cortical potentials (SCPs) in epilepsy patients. Thirty-four patients with intractable focal epilepsy participated in 35 sessions of SCP self-regulation training. The spectral analysis was carried out for the EEG recorded at the same electrode site (Cz) that was used for SCP feedback. The most prominent effect was the increase in the θ2 power (6.0–7.9 Hz) and the relative power decrement in all other frequency bands (particularly δ1, α2, and β2) in transfer trials (i.e., where patients controlled their SCPs without continuous feedback) compared with feedback trials. In the second half of the training course (i.e., sessions 21–35) larger power values in the δ, θ, and α bands were found when patients were required to produce positive versus negative SCP shifts. Both across-subject and across-session (within-subject) correlations between spectral EEG parameters, on the one hand, and SCP data, on the other hand, were low and inconsistent, contrary to high and stable correlations between different spectral variables. This fact, as well as the lack of considerable task-dependent effects during the first part of training, indicates that learned SCP shifts did not directly lead to the specific dynamics of the EEG power spectra. Rather, these dynamics were related to nonspecific changes in patients' brain state.
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