Tucker Tensor Decomposition of Multi-session EEG Data

被引：3

作者：

Rostakova, Zuzana ^{[1
]}

Rosipal, Roman ^{[1
]}

Seifpour, Saman ^{[1
]}

机构：

[1] Slovak Acad Sci, Inst Measurement Sci, Dubravska Cesta 9, Bratislava 84104, Slovakia

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT I | 2020年 / 12396卷

关键词：

Multi-channel electroencephalogram; Sensorimotor rhythms; Tucker model; COMPONENTS;

D O I：

10.1007/978-3-030-61609-0_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

下载

页码：115 / 126

页数：12

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