In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …

Emotion recognition using electroencephalography (EEG) is becoming an interesting topic
among researchers. It has made a remarkable entry in the domain of biomedical, smart …

The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …

This paper introduces a methodology based on deep convolutional neural networks (DCNN)
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …

Electroencephalogram (EEG)-based emotion recognition has gained high attention in Brain-
Computer Interfaces. However, due to the non-linearity and non-stationarity of EEG signals …

Deep brain simulations play an important role to study physiological and neuronal behavior
during Parkinson's disease (PD). Electroencephalogram (EEG) signals may faithfully …

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based
brain computer interface (BCI) to decode electroencephalography (EEG) signals. However …

Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition
from electroencephalogram (EEG) signals plays a vital role in the development of a brain …

Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive
neural activity which can be diagnosed by inspecting the electroencephalography (EEG) …

Abstract In Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …