It is a common practice to study the dynamics of sensory and cognitive processes using event-related potentials (ERPs) measured by placing electrodes on the scalp. These ERPs are very small in comparison with the on-going electroencephalogram (EEG) and are barely visible in the individual trials. Therefore, most ERP research relies on the identification of different waves after averaging several presentations of the same stimulus pattern. Although ensemble averaging improves the signal-to-noise-ratio, it implies a loss of information related to variations between the single-trials. In this thesis, I present an automatic denoising method based on the wavelet transform to obtain single-trial evoked potentials. The method is based on the inter- and intra-scale variability of the wavelet coefficients and their deviations from baseline values. The performance of the method is tested with simulated ERPs and with real visual and auditory ERPs. For the simulated data the method gives a significant improvement in the visualisation of single-trial ERPs as well as in the estimation of their amplitudes and latencies in comparison with the standard denoising technique (Donoho’s thresholding) and in comparison with the noisy single-trials. For the real data, the proposed method helps the identification of single-trial ERPs, providing a simple, automatic and fast tool that allows the study of single-trial responses and their correlations with behaviour. We used our proposed denoising algorithm to study the amplitude modulation of the ERP responses to the flashes of faces and to investigate whether the ERP responses in a visual and an auditory oddball paradigm were due to phase-resetting of on-going EEG (phase-resetting model) or due to additive neural responses adding to the background EEG in response to the stimulus presentation (additive model).
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:579241 |
| Date | January 2013 |
| Creators | Ahmadi, Maryam |
| Contributors | Quiroga, Rodrigo Quian; Ison, Matias |
| Publisher | University of Leicester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2381/28224 |
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