This paper presented the technique of robot control by event-related potentials (ERPs) of brain waves. Based on the proposed technique, severe physical disabilities can free browse outside world. A specific component of ERPs, N2P3, was found and used to control the movement of robot and the view of camera on the designed brain-computer interface (BCI). Users only required watching the stimuli o...

The goal of proposed work is the development of an electroencephalogram (EEG) based BCI system. The overview of this work is, the user thought is extracted from the brain activity of a healthy person. Pre-processing is performed using filters and wavelet transforms to extract the features and classify them to their respective class. The intention of this work is to enhance human interaction wit...

Brain Computer Interface helps and enables people through their mental activities to perform applications and to operate devices. Now-a-days research in this field has matured through many prototypes like brain controlled wheel chairs, games etc. Based on this BCI must come out of the lab to the world and to be applied to real time applications. This makes us to focus on the development on life...

brain-computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing eeg signals measured in different mental states.  therefore, choosing suitable features is demanded for a good bci communication. in this regard, one of the points to be considered is feature vector dimensionality. we present a method of feature reduction us...

As a new way of implementing human-computer interface, brain-computer interfaces (BCI) dramatically change the user experiences and have broad applications in cyber behavior research. This methodological review attempts to provide an overall picture of the BCI science and its role in cyberpsychology. After an introduction of BCI and the literature search methods used in this review, we offer an...

In this paper, we propose a classification algorithm for P300-based Brain Computer Interface (BCI). Brain Computer Interface (BCI) with P300 speller helps Amyotrophic Lateral Sclerosis (ALS) patients to spell words with the help of their brain signal activities. Amyotrophic Lateral Sclerosis (ALS) also known as Lou Gehrig's disease in which certain nerve cells in brain and spinal cord also call...

Using the EEG Motor Movement/Imagery database there is proposed an off-line analysis for a brain computer interface (BCI) paradigm. The purpose of the quantitative research is to compare classifier in order to determinate which of them has highest rates of classification. The power spectral density method is used to evaluate the (de)synchronizations that appear on Mu rhythm. The features extrac...

Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG-based emotion recognition. This was motivated by the recent advances in accuracy and efficiency from applying deep learning techniques in pattern recognition and classification appli...

We have set up a brain-computer interface to be used as an input device to a highly immersive virtual reality Cave-like system. We have carried out two navigation experiments: three subjects were required to rotate in a virtual bar room by imagining left or right hand movement, and to walk along a single dimension in a virtual street by imagining foot or hand movement. In this paper we focus on...