This paper investigates P300 features extracted through wavelet transform for BCI systems. Feature extraction is one of the key issues of signal processing for P300 based brain-computer interface systems (BCI). This paper examines and highlights the significance of using wavelets in P300 based BCI systems. We also mention various methods of feature extraction from P300 signals. The analysis sug...

Recently, methodologies for developing brain-computer interface (BCI) games using the BCI have been actively researched. The existing general framework for processing brain waves does not provide the functions required to develop BCI games. Thus, developing BCI games is difficult and requires a large amount of time. Effective BCI game development requires a BCI game framework. Therefore the BCI...

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatica...

In this study, a novel control algorithm, based on a P300-based brain-computer interface (BCI) is fully developed to control a 2-DoF robotic arm. Eight subjects including 5 men and 3 women perform a 2-dimensional target tracking in a simulated environment. Their EEG (Electroencephalography) signals from visual cortex are recorded and P300 components are extracted and evaluated to perform a real...

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this appro...

This paper aims at developing Brain Computer Interface (BCI) system which uses EEG signals. In this, user thinking is extracted from brain activity of healthy person. Features are extracted from pre-processed signals and classify them into their respective alpha, beta, delta and gamma signal classes. Main intension of this work is to provide better communication interface to a person using brai...

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.

we present a novel and e±cient scheme that selects a minimal set of effective features and channels for detecting the p300 component of the event-related potential in the brain-computer interface (bci) paradigm. for obtaining a minimal set of effective features, we take the truncated coe±cients of discrete daubechies 4 wavelet, and for selecting the effective eeg channels, we utilize an improve...

We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.

In order to bring Brain Computer Interface systems from research laboratories to end users’ homes several factors should be considered and improved. This work aimed to assess performances during repeated ERPs-based BCI sessions in the same day mimicking the use of BCI system as communication aid. Preliminary findings highlighted a daily variability of BCI performances.