A comparative analysis of signal processing and classification methods for different applications based on EEG signals

• Autorzy: Khosla Ashima , Khandnor Padmavati , Chand Trilok

Identyfikatory

DOI

10.1016/j.bbe.2020.02.002

• Języki publikacji: EN

Abstrakty

EN

Electroencephalogram (EEG) measures the neuronal activities in the form of electric currents that are generated due to the synchronized activity by a group of specialized pyramidal cells inside the brain. The study presents a brief comparison of various functional neuroimaging techniques, revealing the excellent neuroimaging capabilities of EEG signals such as high temporal resolution, inexpensiveness, portability, and non-invasiveness as compared to the other techniques such as positron emission tomography, magnetoencephalogram, func-tional magnetic resonance imaging, and transcranial magnetic stimulation. Different types of frequency bands associated with the brain signals are also being summarized. The main purpose of this literature survey is to cover the maximum possible applications of EEG signals based on computer-aided technologies, ranging from the diagnosis of various neurological disorders such as epilepsy, major depressive disorder, alcohol use disorder, and dementia to the monitoring of other applications such as motor imagery, identity authentication, emotion recognition, sleep stage classification, eye state detection, and drowsiness monitoring. After reviewing them, the comparative analysis of the publicly available EEG datasets and other local data acquisition methods, preprocessing techniques, feature extraction methods, and the result analysis through the classification models and statistical tests has been presented. Then the research gaps and future directions in the present studies have been summarized with the aim to inspire the readers to explore more opportunities on the current topic. Finally, the survey has been completed with the brief description about the studies exploring the fusion of brain signals from multiple modalities.

Słowa kluczowe

EN

EEG applications; signal processing; classification model; EEG data collection; Brain rhythms; functional neuroimaging techniques

PL

elektroencefalografia; przetwarzanie sygnału; model klasyfikacyjny; gromadzenie danych EEG; neuroobrazowanie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 649--690
• Opis fizyczny: Bibliogr. 131 poz., rys., tab.

Twórcy

autor

Khosla Ashima

• Computer Science and Engineering Department, Punjab Engineering College (Deemed to be University), Sector 12, Chandigarh, India

autor

Khandnor Padmavati

• Computer Science and Engineering Department, Punjab Engineering College (Deemed to be University), Chandigarh, India

autor

Chand Trilok

• Computer Science and Engineering Department, Punjab Engineering College (Deemed to be University), Chandigarh, India

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Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).