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

Publication date: Available online 21 February 2020Source: Biocybernetics and Biomedical EngineeringAuthor(s): Ashima Khosla, Padmavati Khandnor, Trilok ChandAbstractElectroencephalogram (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, functional 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, fea...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research

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