As driving functions become increasingly automated, motorists run the risk of becoming
cognitively removed from the driving process. Psychophysiological measures may provide …

In this paper, classification of mental task-root brain-computer interfaces (BCIs) is being
investigated. The mental tasks are dominant area of investigations in BCI, which utmost …

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in
designing various brain-computer interface (BCI) applications. Most of the current …

Fatigue driving is the main cause of traffic accidents. Analysis of electroencephalogram
(EEG) signals has attracted wide attention for identifying fatigue driving. With the …

The article is devoted to the classification of brain activity patterns using machine learning
based on EEG data. The aim of the study is to compare the results of machine learning …

Emotion recognition systems are helpful in human–machine interactions and Intelligence
Medical applications. Electroencephalogram (EEG) is closely related to the central nervous …

Brain-Computer Interfaces (BCIs) are an approach that enables humans to interact with their
surroundings by brain generated control signals. Electroencephalographic (EEG) signals …

Spinal cord injury (SCI) is a severely disabling disease, and SCI disrupts connections
between the brain and the spinal cord. Brain-computer interface (BCI) is used in SCI …

This study aims to provide a comprehensive comparison for classification of
Electroencephalography (EEG) signal based motor imagery, in time domain and time …

Electroencephalogram (EEG) signals are low-amplitude electrical signals that measure the
electrical activity between electrodes from the scalp and neurons in the brain. Successful …