Resumo

PETERSON, Victoria et al. DETECTION OF EVENT-RELATED POTENTIALS IN BRAIN-COMPUTER INTERFACES USING THE WAVELET TRANSFORM. Rev. ing. biomed. [online]. 2013, vol.7, n.14, pp.51-59. ISSN 1909-9762.

A brain-computer interface (BCI) is a system that provides a direct communication between the brain of a person and the outside world. For the present work we used an EEG-based event-related evoked potentials BCI. This paper aims to efficiently solve the problem of classification, which has two possible classes: recordings with evoked-potentials (ERP) and recordings without them. We proposed to evaluate the performance of a BCI using the discrete dyadic wavelet transform (DDWT) and the wavelet packet transform (WPT) as feature extraction methods for ERP signal detection. The database consisted of single-epoch EEG recordings from ten healthy subjects. From temporal patterns (recordings without any post-processing), five wavelet patterns were generated after applying DDWT and WPT via different techniques. The performance of the wavelet and temporal patterns were analyzed with the Fisher linear classifier finding that DDWT patterns, filtered at 16 Hz, presented better classification results than temporal patterns. This means that improving the feature extraction step, improves classification, and consequently, the performance of the entire BCI system.

Palavras-chave : Brain-computer interface; Dyadic discrete wavelet transform; Wavelet packet transform.

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