Abstract
The classification of P300 response has been studied extensively in the last decade mainly due to its increasing use in Brain Computer Interfaces (BCIs). Most of the current work involves the classification of P300 response that are produced from the BCI Speller Paradigm. However, this visual stimulation paradigm is ineffective when studying the visual analytics of subjects. Under this situation, studies have shown that the magnitude and latency of the P300 response are affected by the cognitive workload of the task as well as the physiological condition of the subject. In this preliminary study, by using visual oddball paradigm, we investigate and compare the performances of two classifiers, namely the Linear Discriminant Analysis (LDA) and Discrete Hidden Markov Model (DHMM), when the P300 response is affected by variations in magnitude and latency.
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Lee, W.L., Leung, Y.H., Tan, T. (2011). P300 Response Classification in the Presence of Magnitude and Latency Fluctuations. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_43
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DOI: https://doi.org/10.1007/978-3-642-24955-6_43
Publisher Name: Springer, Berlin, Heidelberg
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