Age and Gender Prediction Using Paralinguistic Features of EEG Signals and Machine Learning

The development of brain-computer interfaces (BCI) has sparked significant interest in leveraging electroencephalography (EEG) data for diverse applications. One of the applications is age and gender prediction of individuals. This paper introduces a novel approach that harnesses the potential of ma...

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Bibliographic Details
Main Authors: Alamr, Reema, Alsumari, Walaa, Alotaiby, Turky
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Age prediction
Brain modeling
Brain-computer interfaces
EEG
Electroencephalography
Feature extraction
Gender prediction
Paralinguistic features
Radio frequency
Random Forest
Random forests
Recording
Support vector machines
Vectors
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Summary:The development of brain-computer interfaces (BCI) has sparked significant interest in leveraging electroencephalography (EEG) data for diverse applications. One of the applications is age and gender prediction of individuals. This paper introduces a novel approach that harnesses the potential of machine learning models, specifically Support Vector Machines (SVM) and Random Forest (RF), in conjunction with paralinguistic feature extraction to achieve highly accurate age and gender prediction. This study used a dataset consisting of EEG recordings from 64 subjects in a relaxed position, with both open and closed eyes. By extracting paralinguistic features from the EEG signals, subtle variations in brain activity associated with age and gender differences are captured. Through extensive experimentation and rigorous evaluation, the SVM model demonstrated exceptional performance, achieving an accuracy of 99.61% in both age and gender estimation. These remarkable results highlight the effectiveness of the proposed approach and the potential of paralinguistic feature extraction on EEG data as robust indicators of age and gender.
ISSN:2767-7702
DOI:10.1109/ICCSPA61559.2024.10794274