Recent developments and studies in brain-computer interface (BCI) technologies have
facilitated emotion detection and classification. Many BCI studies have sought to investigate …

Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system
caused due to the loss of dopaminergic neurons. It is classified under movement disorder as …

An automated detection system for Parkinson's disease (PD) employing the convolutional
neural network (CNN) is proposed in this study. PD is characterized by the gradual …

Advances in signal processing and machine learning have expedited
electroencephalogram (EEG)-based emotion recognition research, and numerous EEG …

Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …

Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic
emotional state, resulting in more reliable, natural, and meaningful human-computer …

Background: Parkinson's disease (PD) affects 7–10 million people worldwide. Its diagnosis
is clinical and can be supported by image-based tests, which are expensive and not always …

Emotion electroencephalography (EEG) datasets play a significant role in EEG-based
emotion recognition research, providing a platform for comparisons of different emotion …

Higher-order spectra (HOS) is an efficient feature extraction method used in various
biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities …

Parkinson's disease (PD) is a neurodegenerative disorder that causes severe motor and
cognitive dysfunctions. Several types of physiological signals can be analyzed to accurately …