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- W2295859872 abstract "The cerebral Evoked Potential (EP) signals are commonplace in many fields of neuroscience. They are the electrical outcome of brain’s reception in response to an external stimulus. However, the analysis of the EPs embedded in electroencephalogram (EEG) is normally performed by visual inspection which usually leads to a subjective interpretation of the results. Therefore, objective and quantitative techniques based only on statistical criteria are required. This paper uses the Magnitude-Square Multiple Coherence (MSMC), which belongs to the Objective Response Detection (ORD) class, together with neural and neuro-fuzzy networks in order to improve EEG investigation. The alpha rhythm is defined as the rhythm ranging from 8 to 13 Hz that occurs over the posterior regions of the head during wakefulness. This rhythm generally exhibits higher voltage over the occipital areas. Some findings suggest a functional significance of such a rhythm, since it can be induced (i.e. initiated by but not time-locked to a stimulus) or evoked by an external stimulation. Although it can be entrained by external stimulation, such a cerebral activity does not seem to be previously taken into account in EP detection. To do so, a neural network approach is used to identify the detection rate in EEGs of 15 normal young subjects during photic stimulation collected in posterior regions of the scalp. The neural networks are designed to have the spectral information of two, four or six EEG derivations as input and the detection rate obtained from MSMC as output. The best results are obtained with two inputs, without alpha rhythm band information and by using neural networks (correlation coefficients around 0.98). As a conclusion this work shows that a mixed approach using ORD techniques and neural networks can be successfully used to obtain automatic detection of EPs.." @default.
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- W2295859872 date "2009-01-01" @default.
- W2295859872 modified "2023-09-26" @default.
- W2295859872 title "Detection of Evoked Responses in EEG using Computational Intelligence Tools" @default.
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- W2295859872 doi "https://doi.org/10.1007/978-3-540-89208-3_312" @default.
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