Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

A growing number of affective computing researches recently developed a computer system
that can recognize an emotional state of the human user to establish affective human …

Support vector machine (SVM) has been used widely for classification of
electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as …

This paper describes a discrete wavelet transform-based feature extraction scheme for the
classification of EEG signals. In this scheme, the discrete wavelet transform is applied on …

Feature extraction is an important step in the process of electroencephalogram (EEG) signal
classification. The authors propose a “pattern recognition” approach that discriminates EEG …

Classification of electroencephalogram (EEG) signals is an important task in the brain
computer interface system. This paper presents two combination strategies of feature …

Brain–computer interface (BCI) technology has been studied with the fundamental goal of
hel** disabled people communicate with the outside world using brain signals. In …

Brain computer interface (BCI), has been one of the most popular domains in computing in
the recent years. BCI is a pathway which allows communication between computers and the …

Support vector machines (SVMs) are widely used classifiers for detecting physiological
patterns in human–computer interaction (HCI). Their success is due to their versatility …

In this study, a multiple kernel learning support vector machine algorithm is proposed for the
identification of EEG signals including mental and cognitive tasks, which is a key component …