Open AccessTime domain analysis of electroencephalogram (EEG) signals for word level comprehension in deaf graduates with congenital and acquired hearing loss
Author(s) -
G. Shirly,
Selvaraj Jayaraman
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1070/1/012083
Subject(s) - electroencephalography , comprehension , audiology , computer science , speech recognition , feature (linguistics) , artificial intelligence , support vector machine , hearing loss , pattern recognition (psychology) , psychology , medicine , neuroscience , linguistics , philosophy , programming language
Deafness can be classified on the basis of onset as congenital and acquired hearing loss. The brain is a sensitive part of our body, electrical pulses from the neurons interact with each other, generating brain signals. EEG signals are extensively used for clinical diagnosis for any brain anomalies, language comprehension and performance measurement studies. This study mainly focuses on analysing the word level comprehension in deaf adults in the age group (21 -25 years) using EEG signals. The raw EEG signals were pre-processed and the relevant time domain linear and nonlinear features were extracted and classified using machine learning algorithms. The approximate entropy feature was found to be best suited for finding the comprehension of both congenital and acquired deaf adults. This feature of ISL was observed to be achieving better classification rate with a maximum average accuracy of 96% in both congenital and acquired deaf adults using SVM classifier.