A wavelet-based approach for the extraction of event related potentials from EEG

• Main Authors: Fatourechi, M., Mason, S.G., Birch, G.E., Ward, R.K.
• Format: Conference Proceeding
• Language: English
• Subjects: Applied sciences; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Data mining; Detection, estimation, filtering, equalization, prediction; Electroencephalography; Enterprise resource planning; Exact sciences and technology; Information, signal and communications theory; Knowledge engineering; Medical management aid. Diagnosis aid; Medical sciences; Noise reduction; Signal and communications theory; Signal representation. Spectral analysis; Signal to noise ratio; Signal, noise; Telecommunications and information theory; Training data; Visualization; Wavelet coefficients; Wavelet transforms
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2004.1326363

Event related potentials (ERPs) are of interest to many researchers seeking knowledge about the functions of the brain. ERPs are low-frequency events that are usually obscured in single trial analysis. To visualize these signals; most of the reliable solutions at the present time use the ensemble averages of many single trials. In this paper, a wavelet-based method called statistical coefficient selection (SCS) is used for the extraction of ERPs from EEG signals. Unlike other wavelet-based denoising methods, the current method does not focus on the wavelet coefficients of the signal itself. Instead, it selects the coefficients based on the statistical study of trials from training data sets. Simulation results show the superiority of the proposed SCS method in extracting ERPs in comparison with other filtering approaches.