Emotion recognition based on electroencephalography (EEG) signal features is now one of
the booming big data research areas. As the number of commercial EEG devices in the …

In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for
emotion recognition using a machine learning approach. Up to now, various findings of …

Predicting biomedical outcomes from Magnetoencephalography and
Electroencephalography (M/EEG) is central to applications like decoding, brain-computer …

Existing correlations between features extracted from Electroencephalography (EEG)
signals and emotional aspects have motivated the development of a diversity of EEG-based …

Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other
subjects' data were used for training the classifier, BCI-based neurofeedback practice could …

A prediction model to decode the human brain activities from brain signals is proposed in
this work. The existing works available in the Neuroscience field are higher in the …

Deep learning has greatly enhanced the predictive performance of
magnetoencephalography (MEG) decoding. However, the lack of interpretability has …

Visual decoding is a critical way to understand the face perception mechanism of the brain
in the neuroscience field. Magnetoencephalography (MEG) is a completely noninvasive …

Objective. The objective of the study is to identify phase coupling patterns that are shared
across subjects via a machine learning approach that utilises source space …

Objective: Most BCI (brain-computer interface) studies have focused on detecting motion
intention from a resting state. However, the dynamic regulation of two motion states, which …