Open AccessEEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform
Author(s) -
Gökhan Altan,
Gulcin Inat
Publication year - 2021
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.202112181
Subject(s) - electroencephalography , pattern recognition (psychology) , computer science , artificial intelligence , feature extraction , wavelet transform , frequency domain , wavelet , time domain , time–frequency analysis , feature (linguistics) , speech recognition , decision tree , continuous wavelet transform , discrete wavelet transform , computer vision , psychology , neuroscience , linguistics , philosophy , filter (signal processing)
The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.