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The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain-computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.

The affective state of a speaker can be identified from the prosody of his or her speech. Voice quality is the most important prosodic cue for emotion recognition from short verbal utterances and nonverbal exclamations, the latter conveying pure emotion, void of all semantic meaning. We adopted two context violation paradigms-oddball and priming-to study the event-related brain potentials (ERP) reflecting this recognition process. We found a negative wave, the N300, in the ERPs to contextually incongruous exclamations, and interpreted this component as analogous to the well-known N400 response to semantically inappropriate words. The N300 appears to be a real-time psychophysiological measure of spontaneous emotion recognition from vocal cues, which could prove a useful tool for the examination of affective-prosody comprehension. In addition, we developed a new ERP component detection and estimation method that is based on the continuous wavelet transform (CWT), does not rely on visual inspection of the waveforms, and yields larger statistical difference effects than classical methods.

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