We propose a BCI mouse and speller based on the manipulation of P300 waves in EEG signals. The 2–D motion of the pointer on the screen is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by a GA.

P300 is an electric signal emitted by brain about 300 milliseconds after a rare, but relevant-for-the-user event. One of the applications of this signal is sentence spelling that enables subjects who lost the control of their motor pathways to communicate by selecting characters in a matrix containing all the alphabet symbols. Although this technology has made considerable progress in the last ...

We consider a P300 BCI application where the subjects can write figures and letters in an unsupervised fashion. We (i) show that a generic speller can attain the state-of-the-art accuracy without any training phase or calibration and (ii) present an adaptive setup that consistently increases the bit rate for most of the subjects.

A visual speller is a brain–computer interface that empowers users with limitedmotor functionality to input text into a computer by measuring their electroencephalographic responses to visual stimuli. Most prior research on visual spellers has focused on input of alphabetic text. Adapting a speller for other types of segmental or syllabic script is straightforward because such scripts comprise ...

An error potential (ErrP) is an innate event-related potential generated when a subject makes a mistake, and, more relevant to brain-computer interface (BCI) applications, when the BCI itself behaves differently from the user intent. For this reason, error potentials are nowadays attracting attention in the BCI field and the presence of ErrPs has been studied already in a few BCI paradigms. In ...

HE P300 speller is a brain-computer interface (BCI) system that restores communication ability to “locked-in” patients by detecting evoked responses to visual stimuli in the user’s electroencephalogram (EEG) signal [1]. Choosing the optimal placement and number of electrodes is essential in any EEG system as it balances the amount of available data against the set-up time and the amount of comp...

In this paper, we address the important problem of channel selection for a P300-based brain computer interface (BCI) speller system in the situation of insufficient training data with labels. An iterative semi-supervised support vector machine (SVM) is proposed for time segment selection as well as classification, in which both labeled training data and unlabeled test data are utilized. The per...

The P300 speller is a well known Brain-Computer Interface paradigm that has been used for over two decades. A new P300 speller paradigm (XP300) is proposed. It includes several characteristics: (i) the items are not intensified by using rows and columns, (ii) the order of the visual stimuli is pseudo-random, (iii) a visual feedback is added on each item to increase the stimulus meaning, which i...