This study shows how intelligence of an individual can be determined in different intelligence domains using brain and bio-signals. Results of pupil dilation, eye-blink and EEG (Electroencephalogram) signals were analyzed. It was found that high intelligent individuals (HI) could easily modulate their resource allocation and information processing strategy than low intelligent individuals (LI) ...

Abstract The classifier efficiency of the brain‐computer interface systems is significantly impacted by non‐stationarity electroencephalogram (EEG) signals. We propose an adaptive variant linear discriminant analysis (LDA) as a solution to this problem. This constantly adjusts its parameters account for most recent EEG data. In study, authors will update mean values well covariance matrix each ...

This paper investigates a method to generate personal identification number (PIN) using brain activity recorded from a single active electroencephalogram (EEG) channel. EEG based biometric to generate PIN is less prone to fraud and the method is based on the recent developments in brain-computer interface (BCI) technology, specifically P300 based BCI designs. Our perfect classification accuraci...

A statistical method for finding the optimal preictal period to be used in epileptic seizure prediction algorithms is presented. As supervised machine learning methods need labeled training samples, the adequate selection of preictal period plays a key role in the training of an efficient classifier employed in seizure prediction. The proposed method uses amplitude distribution histograms of a ...

The present study investigates the effect of transcranial sinusoidal stimulation (tACS with positive offset in this article defined s so-tCS) on electroencephalogram (EEG) parameters and cognitive performance in healthy participants. It turned out that so-tCS oes not have any strong effect on cognitive performance but so-tCS increases EEG power in slow oscillations at electrode position z. Sele...

An electroencephalogram (EEG) is often corrupted by different types of artifacts. Many efforts have been made to enhance its quality by reducing the artifact. The EEG contains the technical artifacts (noise from the electric power source, amplitude artifact, etc.) and biological artifacts (eye artifacts, ECG and EMG artifacts). This paper is focused on eyeblinking artifact detection from the vi...

The significance of what is recorded in an electroencephalogram (EEG) can be easily misunderstood or misinterpreted. Even after years of use, it is still not an exact science. There appear to be many possible uses of the EEG but no clear agreement on its best use. Even its role in assessing the treatment, severity, progress and prognosis of epilepsy is varied. The need to find answers to questi...

Haenggi and colleagues report considerable intra- and inter-individual variability in derived electroencephalogram (EEG) parameters (Bispectral Index (BIS), response entropy and state entropy) recorded in volunteers sedated with midazolam or dexmedetomidine infusions titrated to modified Ramsay scores of 2, 3 and 4, and a remifentanil infusion at a fixed target concentration. Possible explanati...

Since its discovery in the late 1920s, the electroencephalogram (EEG) has developed into an array of digitally-based techniques, integrated with video and other investigative modalities that retain a central role in diagnosis and management of patients with seizure disorders. In contrast to the technological advances, relatively little progress has been made in understanding the cerebral genera...

Electroencephalogram (EEG) coherence is a mixture of phase locking interrupted by phase shifts in the spontaneous EEG. Average reference, Laplacian transforms, and independent component (ICA) reconstruction of time series can distort physiologically generated phase differences and invalidate the computation of coherence and phase differences as well as in the computation of directed coherence a...