Tucker Tensor Decomposition of Multi-session EEG Data
| Autor: | Zuzana Rošťáková, Saman Seifpour, Roman Rosipal |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science 05 social sciences Pattern recognition Electroencephalography 050105 experimental psychology Session (web analytics) Interpretation (model theory) Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Eeg data medicine Tensor decomposition 0501 psychology and cognitive sciences Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Artificial Neural Networks and Machine Learning – ICANN 2020 ISBN: 9783030616083 ICANN (1) |
| Popis: | The Tucker model is a tensor decomposition method for multi-way data analysis. However, its application in the area of multi-channel electroencephalogram (EEG) is rare and often without detailed electrophysiological interpretation of the obtained results. In this work, we apply the Tucker model to a set of multi-channel EEG data recorded over several separate sessions of motor imagery training. We consider a three-way and four-way version of the model and investigate its effect when applied to multi-session data. We discuss the advantages and disadvantages of both Tucker model approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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