2009 Special Issue: Selecting features for BCI control based on a covert spatial attention paradigm

Authors:
Marcel van Gerven;Ali Bahramisharif;Tom Heskes;Ole Jensen
Affiliations:
Institute for Computing and Information Sciences, Intelligent Systems Group, The Netherlands;Institute for Computing and Information Sciences, Intelligent Systems Group, The Netherlands;Institute for Computing and Information Sciences, Intelligent Systems Group, The Netherlands;Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands
Venue:
Neural Networks
Year:
2009

Citing 6
Cited 2

The brain response interface: communication through visually-induced electrical brain responses

Journal of Microcomputer Applications - Special issue on computers for handicapped people
Text Categorization Based on Regularized Linear Classification Methods

Information Retrieval
A Fast Dual Algorithm for Kernel Logistic Regression

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust EEG channel selection across subjects for brain-computer interfaces

EURASIP Journal on Applied Signal Processing

Editorial: Recent advances in brain-machine interfaces

Neural Networks
On the use of interaction error potentials for adaptive brain computer interfaces

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Covert attention to spatial locations in the visual field is a relatively new control signal for brain-computer interfaces. Previous EEG research has shown that trials can be classified by thresholding based on left and right hemisphere alpha power in covert spatial attention paradigms. We reexamine the covert attention paradigm based on MEG measurements for fifteen subjects. It is shown that classification performance can be improved by applying sparse logistic regression in order to select a subset of the sensors specific to each subject as the basis for classification. Furthermore, insight is gained into how classification performance changes as a function of the length of the attention period and as a function of the number of trials. Classification performance steadily increases as the length of the attention period over which is averaged is increased, although this does not necessarily translate into higher bit rates. Good classification performance using early components of the attention period may be related to evoked response. With regard to the number of used trials, classification performance became maximal after 150 samples had been obtained, requiring a training time of approximately eleven minutes under the current experimental paradigm.