Wavelet-based Semblance for P300 Single-trial Detection

Carolina Saavedra 1 Laurent Bougrain 1, *
* Corresponding author
1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Electroencephalographic signals are usually contaminated by noise and artifacts making difficult to detect Event-Related Potential (ERP), specially in single trials. Wavelet denoising has been successfully applied to ERP detection, but usually works using channels information independently. This paper presents a new adaptive approach to denoise signals taking into account channels correlation in the wavelet domain. Moreover, we combine phase and amplitude information in the wavelet domain to automatically select a temporal window which increases class separability. Results on a classic Brain-Computer Interface application to spell characters using P300 detection show that our algorithm has a better accuracy with respect to the VisuShrink wavelet technique and XDAWN algorithm among 22 healthy subjects, and a better regularity than XDAWN.
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Carolina Saavedra, Laurent Bougrain. Wavelet-based Semblance for P300 Single-trial Detection. BIOSIGNAL - international conference on Bio-Inspired Systems and Signal Processing - 2013, Feb 2013, Barcelone, Spain. ⟨hal-00756563⟩

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