Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on...

Introduction: The P300 is one of the most widely used brain responses in BCIs today, popularized by none other than the P300 speller itself. However, most systems still require significant subject-specific training to achieve accurate, reliable classification of brain signals. We present an approach to classification that allows for classification with zero subject-specific data and also improv...

The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain-computer interfaces (BCIs) because the users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a...

This paper addresses the problem of signal responses variability within a single subject in P300 speller Brain-Computer Interfaces. We propose here a method to cope with these variabilities by considering a single learner for each acquisition session. Each learner consists of a channel selection procedure and a classifier. Our algorithm has been benchmarked with the data and the results of the ...

The QUT team participated in the NTCIR-13 Neurally Augmented Image Labeling Strategies (NAILS) task, this report describes our approach to solving the problem of developing machine learning models for classifying EEG data from an RSVP image search task. We explore the use of commonly used successful methodologies from the P300 Speller Paradigm, in particular the use of ensembles of support vect...

In this paper the possibility of the electroencephalogram (EEG) compressed sensing based on specific dictionaries is presented. Several types of projection matrices (matrices with random i.i.d. elements sampled from the Gaussian or Bernoulli distributions, and matrices optimized for the particular dictionary used in reconstruction by means of appropriate algorithms) have been compared. The resu...

This paper, investigates the use of a 3D setting for BrainComputer Interface (BCI) by implementing the 3D interface for the P300-Speller device. The 3D configurations were implemented using two different approaches which are called Natural 3D and Parallel 2D. The theoretical analysis concerning these two approaches are provided considering the modifications in speed, accuracy, and capacity. The...

The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain–computer interfaces (BCIs) because the users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a...

The study aimed at revealing electrophysiological indicators of mental workload and fatigue during prolonged usage of a P300 brain-computer interface (BCI). Mental workload was experimentally manipulated with dichotic listening tasks. Medium and high workload conditions alternated. Behavioral measures confirmed that the manipulation of mental workload was successful. Reduced P300 amplitude was ...

Brain-Computer Interfaces (BCI) are communication systems that use brain activity to control a computer or other devices. The BCI system described in this study is based on the P300 speller BCI paradigm designed by Farwell an Donchin in 1988 [1]. A new unsupervised algorithm is proposed in this paper1. It is based on the projection of the raw EEG signal into the estimation of the P300 subspace....