The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide
high quality neuroscientific data for open access to the scientific community. As experienced …

This chapter presents an introductory overview and a tutorial of signal-processing
techniques that can be used to recognize mental states from electroencephalographic (EEG) …

Objective. Deep learning (DL) networks are increasingly attracting attention across various
fields, including electroencephalography (EEG) signal processing. These models provide …

EEG-based automatic emotion recognition can help brain-inspired robots in improving their
interactions with humans. This paper presents a novel framework for emotion recognition …

We present a multi-column CNN-based model for emotion recognition from EEG signals.
Recently, a deep neural network is widely employed for extracting features and recognizing …

This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted
of four different motor imagery tasks, namely the imagination of movement of the left hand …

Epileptic seizures occur due to disorder in brain functionality which can affect patient's
health. Prediction of epileptic seizures before the beginning of the onset is quite useful for …

Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs)
suffer from the limited number of samples and simplified features, so as to produce poor …

Brain–computer interface (BCI) systems do not work for all users. This article introduces a
novel combination of tasks that could inspire BCI systems that are more accurate than …

Detection of movement intention from neural signals combined with assistive technologies
may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a …