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Neural Processing Letters

Erschienen in:

25.05.2021

Deep Learning with ConvNet Predicts Imagery Tasks Through EEG

verfasst von: Gokhan Altan, Apdullah Yayık, Yakup Kutlu

Erschienen in: Neural Processing Letters | Ausgabe 4/2021

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Abstract

Deep learning with convolutional neural networks (ConvNets) has dramatically improved the learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is a rising curiosity in interpreting and analyzing electroencephalography (EEG) dynamics with ConvNets. Our study focused on ConvNets of different structures, the efficiency of multiple machine learning algorithms with optimization on ConvNets, constructing for predicting imagined left and right movements on a subject-independent basis through raw EEG data. We adapted novel lower-upper triangularization based extreme learning machines (LuELM) to the ConvNet architecture. Results showed that recently advanced methods in machine learning field, i.e. adaptive moments and batch normalization together with dropout strategy, improved ConvNets predicting ability, outperforming that of conventional fully-connected neural networks with widely-used spectral features. The proposed prediction model achieved improvements in classification performances with the rates of 90.33%, 91.00%, and 89.67% for accuracy, recall, and specificity, respectively.

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Titel: Deep Learning with ConvNet Predicts Imagery Tasks Through EEG
verfasst von: Gokhan Altan
Apdullah Yayık
Yakup Kutlu
Publikationsdatum: 25.05.2021
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 4/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-021-10533-7

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