This paper presents the results of a user experiment designed to measure the impact of visual distractions when making use of steady-state visually evoked potential (SSVEP)-based braincomputer interfaces (BCI). Also the user experience when using such an interface is measured. A set of small applications was developed to simulate such a BCI using a gaze tracker. The impact of visual distraction...

BCI is one of the most intriguing technologies among other HCI systems, mostly because of its capability of recording brain activities. Spelling BCIs, which help paralyzed people to maintain communication, are one of the striking topics in the field of BCI. In this scientific a spelling BCI system with high transfer rate and accuracy that uses SSVEP signals is proposed.In addition, we suggested...

A flexible silver/carbon nanotube-graphene oxide-polydimethylsiloxane (Ag/CNT-GO-PDMS) patch electrode for recording electroencephalography (EEG) signals and recognizing words is prepared. These patches record EEG under the synergistic sensing mechanism of noncontact capacitance mode CNT-GO-PDMS contact current Ag claws, with low scalp resistance 6.4 kΩ. In occipital region, signal-to-noise rat...

We present a quantitative study of phase entrainment by periodic visual stimuli in biologically inspired neural network. The objective is to understand the neuronal population dynamics that underlie brain oscillations external stimuli, which used for therapeutic treatment neurological disorders, example Parkinsonian tremor. Yet, underpinning such not fully understood. Rhythmic sensory stimulati...

Understanding the integration of top-down and bottom-up signals is essential for the study of perception. Current accounts of predictive coding describe this in terms of interactions between state units encoding expectations or predictions, and error units encoding prediction error. However, direct neural evidence for such interactions has not been well established. To achieve this, we combined...

The paper presents an EEG-based wireless brain-computer interface (BCI) with which subjects can mindspell text on a computer screen. The application is based on detecting steady-state visual evoked potentials (SSVEP) in EEG signals recorded on the scalp of the subject. The performance of the BCI is compared for two different classification paradigms: synchronous and asynchronous modes.