%0 Journal Article %1 Laskaris %A Laskaris, N. A. %A Fotopoulos, S. %A Ioannides, A.A. %D 2004 %J Signal Processing Magazine, IEEE %K event information mining recordings related %N 3 %P 66-77 %R 10.1109/MSP.2004.1296544 %T Mining information from event-related recordings %V 21 %X In this article we describe a signal-processing framework for mining information from event-related recordings. Pattern-analytic tools are combined with graph-theoretic techniques and signal understanding methodologies in a user-friendly environment with the scope of learning, parameterization, and representation of the ST data manifold. Through the first part, we provide a general outline of our methodological approach while trying to demonstrate all the different stages, where DM tools can be applied. In the second part, we provide a more detailed demonstration, give a synopsis of the obtained results and take the opportunity to underline the merits of the adopted algorithmic procedures. To enable the full justification of our framework, instead of just including a technical demonstration of some of the incorporated DM and KDD tools, we address the problem of response variability: an issue of great neuroscientific importance and the subject of continuous debate. The major question in all the previous studies was the validity of "signal plus noise" model, i.e., whether a stereotyped evoked response is linearly superimposed on the ongoing brain activity after every stimulus presentation, a prerequisite for the validity of ensemble-averaging. Using data from a simple visual experiment targeting at the early neuromagnetic response known as N70m, we try to bridge the gap between the "conservative-party" that suggests heavy averaging as the only way to study the brain's response and the "neurodynamics-party" that claims the averaged-signal has very little to say about how the real-time processing of an input from a sensory pathway is actually performed in the cortex.

@article{Laskaris, abstract = {In this article we describe a signal-processing framework for mining information from event-related recordings. Pattern-analytic tools are combined with graph-theoretic techniques and signal understanding methodologies in a user-friendly environment with the scope of learning, parameterization, and representation of the ST data manifold. Through the first part, we provide a general outline of our methodological approach while trying to demonstrate all the different stages, where DM tools can be applied. In the second part, we provide a more detailed demonstration, give a synopsis of the obtained results and take the opportunity to underline the merits of the adopted algorithmic procedures. To enable the full justification of our framework, instead of just including a technical demonstration of some of the incorporated DM and KDD tools, we address the problem of response variability: an issue of great neuroscientific importance and the subject of continuous debate. The major question in all the previous studies was the validity of "signal plus noise" model, i.e., whether a stereotyped evoked response is linearly superimposed on the ongoing brain activity after every stimulus presentation, a prerequisite for the validity of ensemble-averaging. Using data from a simple visual experiment targeting at the early neuromagnetic response known as N70m, we try to bridge the gap between the "conservative-party" that suggests heavy averaging as the only way to study the brain's response and the "neurodynamics-party" that claims the averaged-signal has very little to say about how the real-time processing of an input from a sensory pathway is actually performed in the cortex.}, added-at = {2014-04-22T12:45:19.000+0200}, author = {Laskaris, N. A. and Fotopoulos, S. and Ioannides, A.A.}, biburl = {https://www.bibsonomy.org/bibtex/21d88821ef2d3ec4e76a62b5d86fa0329/talli}, doi = {10.1109/MSP.2004.1296544}, interhash = {fe504b9436fec8429406f0055a78184d}, intrahash = {1d88821ef2d3ec4e76a62b5d86fa0329}, issn = {1053-5888}, journal = {Signal Processing Magazine, IEEE}, keywords = {event information mining recordings related}, month = may, number = 3, pages = {66-77}, timestamp = {2014-11-02T20:23:36.000+0100}, title = {Mining information from event-related recordings}, volume = 21, year = 2004 }