Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). Wit...

In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain–computer interface (BCI) task. We expected that MMI would harness present moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed MMI would facilitate increases in task accuracy and promote the production...

This paper focuses on the development of a P300 speller and the design of a rehabilitation robot using a brain-machine interface. The combined feature set provides a norm that can be used to assess trends of the user’s increased or decreased independence. The combined feature set is found to maintain a 90% sorting rate; it can also reduce the relationship of individual independence for each sub...

P300 CLASSIFICATION USING DEEP BELIEF NETS Electroencephalogram (EEG) is measure of the electrical activity of the brain. One of the most important EEG paradigm that has been explored in BCI systems is the P300 signal. The P300 wave is an endogenous event-related-potential which can be captured during the process of decision making as a subject reacts to a stimulus. One way to detect the P300 s...

In a recent study by Sellers et al. (1) a patient in locked-in state after brainstem stroke was able to successfully communicate via electroencephalography (EEG) based brain computer interface (BCI) employing a P300 speller paradigm. BCI uses the brain signal, which can either be the electrical signal (electroencephalography-EEG) (2) or a change in hemodynamic activity [functional magnetic reso...

The paper presents a k-means based semi-supervised clustering approach for recognizing and classifying P300 signals for BCI Speller System. P300 signals are proved to be the most suitable Event Related Potential (ERP) signal, used to develop the BCI systems. Due to non-stationary nature of ERP signals, the wavelet transform is the best analysis tool for extracting informative features from P300...

Wavelet denoising has been successfully applied to Event-Related Potential (ERP) detection, but it usually works using channels information independently. This paper presents an adaptive approach to denoise signals taking into account the channels correlation in the wavelet domain. Moreover, we combine phase and amplitude information to automatically select a time window which increases ERP det...

This article presents a new computational intelligence technique for pattern recognition of graphic elements (e.g. event-related potential, auditory evoked potential, kcomplex, spindle) embedded in electro-encephalographic signals. More precisely, we have extended the learning vector quantization (LVQ) algorithm by Kohonen to nonidentity assignment to robustly detect evoked potentials in a nois...

The usability of Brain Computer Interfaces (BCI) based on the P300 speller is severely hindered by the need for long training times and many repetitions of the same stimulus. In this contribution we introduce a set of unsupervised hierarchical probabilistic models that tackle both problems simultaneously by incorporating prior knowledge from two sources: information from other training subjects...