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Get a New Perspecti...
Get a New Perspective on EEG: Convolutional Neural Network Encoders for Parametric t-SNE
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- Svantesson, Mats, 1975- (författare)
- Linköpings universitet,Centrum för social och affektiv neurovetenskap,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Neurofysiologiska kliniken US
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- Olausson, Håkan, 1965- (författare)
- Linköpings universitet,Medicinska fakulteten,Centrum för social och affektiv neurovetenskap,Region Östergötland, Neurofysiologiska kliniken US
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- Eklund, Anders, 1981- (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Statistik och maskininlärning
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visa fler...
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- Thordstein, Magnus (författare)
- Linköpings universitet,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Medicinska fakulteten,Avdelningen för neurobiologi,Region Östergötland, Neurofysiologiska kliniken US
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visa färre...
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(creator_code:org_t)
- 2023-03-07
- 2023
- Engelska.
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Ingår i: Brain Sciences. - : MDPI. - 2076-3425. ; 13:3
- Relaterad länk:
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https://doi.org/10.3...
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- EEG; deep learning; convolutional neural networks; t-SNE; categories
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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