Entropy-based feature extraction for classification of EEG signal using Lifting Wavelet Transform

被引：4

作者：

Ananthi, A. ^{[1
,4
]}

Subathra, M. S. P. ^{[1
,4
]}

George, S. Thomas ^{[2
,4
]}

Sairamya, N. J. ^{[3
,5
]}

机构：

[1] Karunya Inst Technol & Sci, Dept Robot Engn, Coimbatore, Tamilnadu, India

[2] Karunya Inst Technol & Sci, Dept Biomed Engn, Coimbatore, Tamilnadu, India

[3] Karunya Inst Technol & Sci, Dept Dept Psychol, Coimbatore, Tamilnadu, India

[4] Karunya Inst Technol & Sci, Coimbatore, Tamilnadu, India

[5] Univ Calgary, Calgary, AB, Canada

来源：

PRZEGLAD ELEKTROTECHNICZNY | 2024年 / 100卷 / 09期

关键词：

Brain Computer Interface; EEG; Lifting Wavelet Transform; LSTM; MOTOR IMAGERY;

D O I：

10.15199/48.2024.09.27

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In the realm of Brain-Computer Interface (BCI), a crucial hurdle lies in effectively classifying Motor Imagery (MI) signals. Numerous techniques have been developed for Electroencephalogram (EEG) signal-based MI classification. The proposed system transforms EEG signals into various representations through Lifting Wavelet Transform (LWT). Long Short Term Memory (LSTM) is employed for classifying the extracted feature vectors in each line. The performance of this method is evaluated on the PhysioNet database, specifically for distinguishing between right and left hand imagery move. The strategy,resulting in 100% accuracy in 19 out of 72 wavelet families of LWT. This combination proves to be a highly efficient tool for BCI-based EEG analysis, showcasing its potential as a resourceful solution in this domain.

引用

页码：146 / 150

页数：5

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