Brain-computer interfaces (BCI) are communication system that use brain activities to control a device. The BCI studied is based on the P300 speller [1]. A new algorithm to select relevant sensors is proposed: it is based on a previous proposed algorithm [2] used to enhance P300 potentials by spatial filters. Data recorded on three subjects were used to evaluate the proposed selection method: i...

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...

Brain-computer interfaces (BCI) are communication system that use brain activities to control a device. The BCI studied is based on the P300 speller [1]. A new algorithm to select relevant sensors is proposed: it is based on a previous proposed algorithm [2] used to enhance P300 potentials by spatial filters. Data recorded on three subjects were used to evaluate the proposed selection method: i...

Trigram language models are compressed using a Golomb coding method inspired by the original Unix spell program. Compression methods trade off space, time and accuracy (loss). The proposed HashTBO method optimizes space at the expense of time and accuracy. Trigram language models are normally considered memory hogs, but with HashTBO, it is possible to squeeze a trigram language model into a few...