Temporal Processing of Brain Activity for the Recognition of EEG Patterns

Authors:
Alexandre Hauser;Pierre-Edouard Sottas;José del R. Millán
Affiliations:
-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 1
Cited 2

A local neural classifier for the recognition of EEG patterns associated to mental tasks

IEEE Transactions on Neural Networks

Brain-actuated interaction

Artificial Intelligence
Non-invasive brain-actuated control of a mobile robot

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses three common strategies to incorporate temporal dynamics of brain activity to recognize 3 mental tasks from spontaneous EEG signals. The networks have been tested in a hard experimental setup; namely, generalization over different recording sessions while analyzing short time windows. It turns out that the simple local neural classifier currently embedded in our BCI, which averages the response to 8 consecutive EEG samples, is to be preferred to more complex time-processing networks such as TDNN and Elman-like. With this local classifier, users with some hours of training are able to operate several brain-actuated applications.