Wavelet components of auditory event related potentials (ERPs) in relation with signal discrimination, motor response, response inhibition and updating of working memory

Keskin, H. Y.1, Demiralp, T.1, Ademoğlu, A.2, and Ergen, M.1
1Istanbul University, Istanbul Faculty of Medicine, Department of Physiology, Turkey; 2Boğaziši University, Institute of Biomedical Engineering, Turkey
E-mail: h_yaseminkeskin@yahoo.com

ERPs are generated by parallel and sequential activation of different neuronal groups in the brain during cognitive processes. Time-frequency analysis is a powerful method to investigate both consecutive and temporally overlapping signals with distinct frequency characteristics. Assuming that distinct neuronal structures responsible for specific subprocesses of a cognitive operation may operate in different frequency bands, this study aims to decompose auditory ERPs into specific signal components that reflect signal discrimination, motor response, response inhibition, and updating of working memory. Data obtained from 16 healthy volunteers using single-stimulus, oddball, go/no-go, and three-stimulus paradigms were decomposed by wavelet transform into six sets of coefficients. Alpha, theta, and delta band coefficients in the post-stimulus periods were evaluated in parallel with the conventional P200 and P300 peak measurements. Time-domain analyses reveal that discrimination process decreases the P200 amplitude, and motor response, response inhibition and updating of working memory processes increase the P300 amplitudes. Time-frequency analysis refined these results by showing that a prominent alpha oscillation between 0 and 500 ms occurred with a central maximum only in non-discrimination conditions, whereas it was strongly suppressed and two bitemporal alpha foci remained between 125 and 312 ms in discrimination conditions. Furthermore, in 250-375 ms interval a right temporal theta component in relation with discrimination and working memory update, and a parietal delta component that was diminished by signal discrimination and motor response were distinguished. Thus, signal components and topographies related more specifically to different subprocesses of stimulus processing could be obtained by decomposing ERPs using wavelet transform.