Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, ...

Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, ...

Background: Cultural neuroscience is an emerging research field concerned with studying the influences of different cultures on brain anatomy and function. In this study, we examined whether different cultural or genetic influences might influence the resting state electroencephalogram (EEG) in young children (mean age 10 years) from Switzerland and Saudi Arabia. Methods: Resting state EEG reco...

A system capable of reliably detecting lapses in responsiveness ('lapses') has the potential to increase safety in many occupations. We have developed an approach for detecting the state of lapsing with second-scale temporal resolution using data from 15 subjects performing a one-dimensional (1D) visuomotor tracking task for two 1 h sessions while their electroencephalogram (EEG), facial video,...

Decoding experimental conditions from single trial Electroencephalographic (EEG) signals is becoming a major challenge for the study of brain function and real-time applications such as Brain Computer Interface. EEG source reconstruction offers principled ways to estimate the cortical activities from EEG signals. But to what extent it can enhance informative brain signals in single trial has no...

the brain – computer interface (bci) provides a communicational channel between human and machine. most of these systems are based on brain activities. brain computer-interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. the success of this methodology depends on the selection of methods to process the brain signals in each pha...

we propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms (ieeg), which is based on the analysis of system dynamics. in the proposed scheme, the largest lyapunov exponent of the discrete wavelet packet transform (dwpt) of the segmented eeg signals is considered as the discriminating features. such features are processed by a support vector ...

The EEG is a valuable tool because it reflects cerebral physiology, it is a continuous and non-invasive measure, and it changes markedly on the administration of anesthetic drugs. The objective of this project is to find the excellent features to discriminate between different anesthesia states. Spectral Edge Frequency (SEF), spectral entropy and bicoherence can be used to differentiate differe...

Abstract In motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance classification performance imagery BCIs. However, problem subject-specific bandpass frequency selection frequently arises in manifold-based methods. this study, we propose a multiple graph fusion (MRGF) mod...

Recent studies have addressed stress level classification via electroencephalography (EEG) and machine learning. These works typically use EEG-based features, like power spectral density (PSD), to develop classifiers. Nonetheless, these classifiers are usually limited the discrimination of two (stress no stress) or three (low, medium, high) levels. In this study we propose an alternative for qu...