Traditional machine learning methods suffer from severe overfitting in EEG-based emotion
reading. In this paper, we use hierarchical convolutional neural network (HCNN) to classify …

Music has been proven to be a valuable tool for the understanding of human cognition,
human emotion, and their underlying brain mechanisms. The objective of this study is to …

For several decades, a number of methods have been developed for the noninvasive
assessment of the level of consciousness during general anesthesia. In this paper …

This paper makes an outline case for the need for a low-cost, easy to administer method for
detecting dementia within the growing at risk population. It proposes two methods for …

Detrended fluctuation analysis (DFA) is a popular tool in physiological and medical studies
for estimating the self-similarity coefficient, α, of time series. Recent researches extended its …

Recent advances in nonlinear analytic methods for electroencephalography have clarified
the reduced complexity of spatiotemporal dynamics in brain activity observed in Alzheimer's …

The ability to predict seizures may enable patients with epilepsy to better manage their
medications and activities, potentially reducing side effects and improving quality of life …

Epileptogenic human hippocampus generates spontaneous energy fluctuations with a wide
range of amplitude and temporal variation, which are often assumed to be entirely random …

Objective. The power spectrum of the human electroencephalogram (EEG) as a function of
frequency is a mix of brain oscillations (Osc)(eg alpha activity around 10 Hz) and non-Osc or …

Fractal nature of the human sleep EEG was revealed recently. In the literature there are
some attempts to relate fractal features to spectral properties. However, a comprehensive …