Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques

Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques

Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential. However, the inherent noise and complexity of EEG signals pose significant challenges. This study invest...

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Bibliographic Details
Main Authors: Nidhi Patel, Jaiprakash Verma, Swati Jain
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Neuroinformatics
Subjects:
alcoholism
deep learning
machine learning
noise filtering for EEG data
brain computer interface
Online Access:https://www.frontiersin.org/articles/10.3389/fninf.2025.1618050/full
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