Anisha, S and Rahul, D.R (2024) EEG Based Deep Learning Model for Automated Classification of Frequent and Infrequent Readers. EEG Based Deep Learning Model for Automated Classification of Frequent and Infrequent Readers. pp. 117-122.

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Abstract

Reading comprehension is a complex cognitive pro
cess that involves semantic decoding, and schema integration
influenced by reading practice. This study investigates the neural
correlates of reading comprehension and explores how brain
activation patterns differ between frequent and infrequent read
ers. Using Electroencephalography (EEG), the brain activation
patterns were recorded from participants while performing the
reading comprehension tasks. The EEG signal is recorded with
a sampling rate of 1024 Hz to capture the dynamic changes in
brain activation during reading tasks, revealing how frequent
and infrequent readers differ in their cognitive processing and
neural efficiency. The 21 channels of EEG signal acquired from 9
participants were pre-processed to obtain a 512 Hz band-limited
signal. The pre-processed EEG signals were segmented before
being fed into a twenty-four-layered convolutional neural network
(CNN) model. The convolution stage extracts the temporal and
spatial features, and the most significant features are extracted
at the max-pooling stage. Frequent and infrequent readers are
classified using the fully connected layer based on their EEG
signals. The proposed model obtained a classification accuracy
of 93.94%

Item Type: Article
Uncontrolled Keywords: —Reading comprehension, Frequent readers, Infre quent readers, Electroencephalogram, Deep learning algorithm
Divisions: PSG College of Arts and Science > Department of English
Depositing User: Dr. B Sivakumar
Date Deposited: 06 Dec 2025 05:06
Last Modified: 06 Dec 2025 05:06
URI: https://ir.psgcas.ac.in/id/eprint/2564

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