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

• Autorzy: Ananthi A. , Subathra M.S.P. , George S. Thomas , Sairamya N.J.

Identyfikatory

DOI

10.15199/48.2024.09.27

Warianty tytułu

PL

Ekstrakcja cech oparta na entropii do klasyfikacji sygnału EEG przy użyciu transformacji falkowej Lifting Wavelet

• Języki publikacji: EN

Abstrakty

EN

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.

PL

W obszarze interfejsu mózg-komputer (BCI) kluczową przeszkodą jest skuteczna klasyfikacja sygnałów obrazowania motorycznego (MI). Opracowano liczne techniki klasyfikacji MI na podstawie sygnału elektroencefalogramu (EEG). Proponowany system przekształca sygnały EEG na różne reprezentacje za pomocą transformacji falkowej Lifting Wavelet Transform (LWT). Pamięć długoterminowa Long Short Term Memory (LSTM) jest wykorzystywana do klasyfikowania wyodrębnionych wektorów cech w każdej linii. Wydajność tej metody jest oceniana w bazie danych PhysioNet, w szczególności w celu rozróżnienia ruchu obrazowania prawej i lewej ręki. Strategia ta zapewnia 100% dokładność w 19 z 72 rodzin falek LWT. Ta kombinacja okazuje się wysoce wydajnym narzędziem do analizy EEG opartej na BCI, pokazując swój potencjał jako zasobnego rozwiązania w tej dziedzinie.

Słowa kluczowe

EN

brain computer interface; EEG; lifting wavelet transform; LSTM

PL

interfejs mózg-komputer; EEG; Lifting Wavelet Transform; LSTM

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 9
• Strony: 146--150
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Ananthi A.

• Department of Robotics Engineering
• Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India

autor

Subathra M.S.P.

• Department of Robotics Engineering
• Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India

autor

George S. Thomas

• Department of Biomedical Engineering
• Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India

autor

Sairamya N.J.

• Department of Psychology
• University of Calgary, Calgary, Canada

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).