Abstract
The processes giving rise to an event related potential engage several evoked and induced oscillatory components, which reflect phase or non-phase locked activity throughout the multiple trials. The separation and identification of such components could not only serve diagnostic purposes, but also facilitate the design of brain-computer interface systems. However, the effective analysis of components is hindered by many factors including the complexity of the EEG signal and its variation over the trials. In this paper we study several measures for the identification of the nature of independent components and address the means for efficient decomposition of the rich information content embedded in the multi-channel EEG recordings associated with the multiple trials of an event-related experiment. The efficiency of the proposed methodology is demonstrated through simulated and real experiments.
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References
Onton, J., Makeig, S.: Information-based modeling of event-related brain dynamics. Elsevier Progress in Brain Research book series (February 2006)
Tallon-Baudry, C., Bertrand, O.: Oscillatory gamma activity in humans and its role in object representation. Trends in Cognitive Sciences 3(4), 151–162 (1999)
Makeig, S., Debener, S., Onton, J., Delorme, A.: Mining Event-Related Brain Dynamics. Trends Cogn. Sci. 8, 204–210 (2004a)
Jung, T.P., Makeig, S., et al.: Imaging Brain Dynamics Using Independent Component Analysis. Proc. of the IEEE 89(7), 1107–1122 (2001)
Tsai, A.C., Liou, M., Jung, T., Onton, J.A., Cheng, P.E., Huang, C., Duann, J., Makeig, S.: Mapping single trial EEG records on the cortical surface through a spatiotemporal modality. NeuroImage 32, 195–207 (2006)
Bell, A.J., Sejnowski, T.J.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Comput. 7, 1129–1159 (1995)
Bernat, E.M., Williams, W.J., Gehring, W.J.: Decomposing ERP Time-Frequency Energy using PCA. Clinical Neurophysiology 116, 1314–1334 (2005)
Tallon-Baudry, C., Bertrand, O., Delpuech, C., Pernier, J.: Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human. The Journal of Neuroscience 16(13), 4240–4249 (1996)
Makeig, S., Delorme, A., Westerfield, M., Jung, T., Townsend, J., Courchesne, E., Sejnowski, T.J.: Electroencephalographic Brain Dynamics following manually responded visual targets. Plos Biology 2, 747–762 (2004)
Polich, J.: Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology 118, 2128–2148 (2007)
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Iordanidou, V., Michalopoulos, K., Sakkalis, V., Zervakis, M. (2009). Decomposition Methods for Detailed Analysis of Content in ERP Recordings. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_37
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DOI: https://doi.org/10.1007/978-3-642-04277-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
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