Abstract
Based on similarity measures in the wavelet domain under a multichannel EEG setting, two new methods are developed for single-trial event-related potential (ERP) detection. The first method, named “multichannel EEG thresholding by similarity” (METS), simultaneously denoises all of the information recorded by the channels. The second approach, named “semblance-based ERP window selection” (SEWS), presents two versions to automatically localize the ERP in time for each subject to reduce the time window to be analysed by removing useless features. We empirically show that when these methods are used independently, they are suitable for ERP denoising and feature extraction. Meanwhile, the combination of both methods obtains better results compared to using them independently. The denoising algorithm was compared with classic thresholding methods based on wavelets and was found to obtain better results, which shows its suitability for ERP processing. The combination of the two algorithms for denoising the signals and selecting the time window has been compared to xDAWN, which is an efficient algorithm to enhance ERPs. We conclude that our wavelet-based semblance method performs better than xDAWN for single-trial detection in the presence of artifacts or noise.
Highlights
Brain-computer interface (BCI) research endeavours to provide new ways of communication for severely handicapped people by translating their brain activity into commands that can be used in a computer or other devices, without using the standard peripheral nerves and muscular pathways
We introduce a novel method to denoise, localize, and isolate event-related potential (ERP) combining two approaches based on wavelet theory. is formalism is used to study singletrial brain signals based on similarity measures. e first approach simultaneously denoises the signals by using the phase information provided by all the channels in a single trial
When comparing with the baseline filter results, the combination of our two independent algorithms significantly improves the ERP detection in a single trial and only incurs a small computational cost compared to the time required for the training and classification phases
Summary
Brain-computer interface (BCI) research endeavours to provide new ways of communication for severely handicapped people by translating their brain activity into commands that can be used in a computer or other devices, without using the standard peripheral nerves and muscular pathways. In [34], a single-trial P300 detection algorithm is presented based on independent component analysis (ICA) and wavelets. Despite these advances, singletrial P300 detection still needs to be improved before it can be made more available for the general public. The second approach combines the phase and the amplitude information of the signals to optimize the time window of the ERP for each user. E rest of this paper is organized as follows: In Section 2, we presented wavelet theory and semblance analysis to introduce our proposal of using the correlated information of recorded channels to remove noise and automatically establish an appropriate time window for the analysis of each subject.
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