EEG Analysis with Wavelet Transform under Music Perception Stimulation

In order to improve the classification accuracy and reliability of emotional state assessment and provide support and help for music therapy, this paper proposes an EEG analysis method based on wavelet transform under the stimulation of music perception. Using the data from the multichannel standard emotion database (DEAP), α, ß, and θ rhythms are extracted in frontal (F3 and F4), temporal (T7 and T8), and central (C3 and C4) channels with wavelet transform. EMD is performed on the extracted EEG rhythm to obtain intrinsic mode function (IMF) components, and then, the average energy and amplitude difference eigenvalues of IMF components of EEG rhythm waves are further extracted, that is, each rhythm wave contains three average energy characteristics and two amplitude difference eigenvalues so as to fully extract EEG feature information. Finally, emotional state evaluation is realized based on a support vector machine classifier. The results show that the correct rate between no emotion, positive emotion, and negative emotion can reach more than 90%. Among the pairwise classification problems among the four emotions selected, the classification accuracy obtained by this EEG feature extraction method is higher than that obtained by general feature extraction methods, which can reach about 70%. Changes in EEG α wave power were closely correlated with the polarity and intensity of emotion; α wave power varied significantly between “happiness and fear,” “pleasure and fear,” and “fear and sadness.” It has a good application prospect in both psychological and physiological research of emotional perception and practical application.

EEG Characteristics to Hyperventilation by Age and Sex in Patients With Various Neurological Disorders

Introduction: Hyperventilation provocation test(s) (HPT) concomitant to electroencephalography (EEG) may detect hidden disorders of the nervous system (CNS). There are various types of abnormal EEG in responses to HPT that provoke different interpretations. However, it is not evident how the onset time of pathological EEG responses to hyperventilation (PERH) reveals dysfunction of the CNS in humans. It is also not clear if age and biological sex affect EEG characteristics in response to HPT. Our previous studies have revealed three types of PERH (disorganization of basic rhythm, paroxysmal discharges, epileptiform activity) concerning the manifestation time of first, second, and third minutes. The current work aims to classify the PERH with regards to age (3–6, 7–12, 13–18, 19–30, 31–50, 50 > year) and the biological sex of the patients.Methods: This study examined the EEG of 985 outpatients with various functional disorders of the CNS. The patients were assigned to one of three experimental groups based on the time occurrence of PERH in response to the HPT.Results: The disorganized basic EEG rhythm in the first, second, third minute of HPT was observed across all age and sex groups. All three types of PERH in the first minute were comparable for both sexes. However, some discrepancies between females compared to males were observed in the second and third minutes. All three types of PERH in the first and the second minutes were found only in women. The second type of PERH has revealed at the second minute of PHT in 13–18-year-old five girls.Conclusion: The three main types of PERH were detected at the first minute in all age groups and sex in patients with various CNS dysfunctions. It is diagnostically informative should be used as a marker during the monitoring of treatment. The specific activity of the brain's response to HPT depends on time, age, sex. The data indicate that taking into account sex differences and age during HPT leads to better results. The sensitivity and severity of the NS reaction toward hypocapnia, stress, and emotion increase in women. Therefore, in such cases should not be recommended to expand functional loads.

The Role of EEG in the Diagnosis, Prognosis and Clinical Correlations of Dementia with Lewy Bodies—A Systematic Review

Despite improvements in diagnostic criteria for dementia with Lewy bodies (DLB), the ability to discriminate DLB from Alzheimer’s disease (AD) and other dementias remains suboptimal. Electroencephalography (EEG) is currently a supportive biomarker in the diagnosis of DLB. We performed a systematic review to better clarify the diagnostic and prognostic role of EEG in DLB and define the clinical correlates of various EEG features described in DLB. MEDLINE, EMBASE, and PsycINFO were searched using search strategies for relevant articles up to 6 August 2020. We included 43 studies comparing EEG in DLB with other diagnoses, 42 of them included a comparison of DLB with AD, 10 studies compared DLB with Parkinson’s disease dementia, and 6 studies compared DLB with other dementias. The studies were visual EEG assessment (6), quantitative EEG (35) and event-related potential studies (2). The most consistent observation was the slowing of the dominant EEG rhythm (<8 Hz) assessed visually or through quantitative EEG, which was observed in ~90% of patients with DLB and only ~10% of patients with AD. Other findings based on qualitative rating, spectral power analyses, connectivity, microstate and machine learning algorithms were largely heterogenous due to differences in study design, EEG acquisition, preprocessing and analysis. EEG protocols should be standardized to allow replication and validation of promising EEG features as potential biomarkers in DLB.