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Get a New Perspective on EEG: Convolutional Neural Network Encoders for Parametric t-SNE

Authors :
Svantesson, Mats
Olausson, Håkan
Eklund, Anders
Thordstein, Magnus
Svantesson, Mats
Olausson, Håkan
Eklund, Anders
Thordstein, Magnus
Publication Year :
2023

Abstract

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.<br />Funding: Linkoping University; University Hospital of Linkoeping; ALF of Region OEstergoetland [LIO-936176, ROE-941359]; ITEA3/VINNOVA

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1399551307
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.3390.brainsci13030453