Key result
CTGAN synthetic data augmentation boosts neural network accuracy for classifying EEG radiofrequency exposure by ~60%.
Absolute Event Rate: 69% vs 43%
Synthetic data augmentation using CTGAN improves the accuracy of Feedforward Neural Networks in classifying EEG signals under radiofrequency exposure.
May enhance EEG classification models in RF research; leaves open prospective validation before any clinical use.
Electroencephalogram (EEG) signal classification plays a critical role in understanding brain activity under varying radiofrequency (RF) exposure conditions. The demand for improving EEG classification modeling is increasing due to the growing number of gadgets emitting RF exposure. Current Electroencephalogram (EEG) classification methods struggle to process signals accurately because of insufficient data and limited variety in the datasets used to train models. This study aims to improve Electroencephalogram (EEG) signal classification using synthetic data and a Feedforward Neural Network (FNN). The goal is to analyze brain signals under different RF exposure conditions and increase classification accuracy using asymmetry ratio analysis and synthetic data for 3 exposure groups. Conditional Generative Adversarial Networks (CTGAN) are employed to generate 88 synthetic samples from an original dataset of 97 EEG recordings, stratified by exposure type and gender. The two sub-bands from the original data were used to generate synthetic data, divided into 3 groups: Left Exposure (LE), Right Exposure (RE), and Sham Exposure (SE). Each group was further split into male and female categories. Statistically significant variations in alpha- and beta-band activity are observed during RF exposure, validating the discriminative capability of the extracted features. Including synthetic data significantly improves classification performance, particularly during male exposure sessions, where the Feedforward Neural Network (FNN) achieves 98% training accuracy. The findings demonstrate that synthetic data augmentation is an effective strategy for improving Electroencephalogram (EEG) classification reliability and offers strong potential for radiofrequency (RF) exposure analysis and related biomedical applications. ABSTRAK: Kajian ini bertujuan menambah baik klasifikasi isyarat Elektroensefalogram (EEG) bagi aktiviti otak di bawah pendedahan frekuensi radio (RF) menggunakan data sintetik dan Rangkaian Nueral Suap-Depan (FNN). Isu utama adalah kekurangan data dalam set data EEG sedia ada. Kami menggunakan analisis nisbah asimetri bagi mengekstrak ciri dan Analisis Varians (ANOVA) dan membandingkan gelombang alfa dan beta antara lelaki dan wanita dalam tiga kumpulan pendedahan: Kiri (LE), Kanan (RE), dan Palsu (SE). Rangkaian Berseteru Generatif Bersyarat (CTGAN) digunakan bagi menjana 88 data sintetik, meningkatkan set data asal 97 sampel. Penggunaan data sintetik meningkatkan ketepatan klasifikasi model, terutama untuk sesi lelaki, mencapai 98% ketepatan latihan dan 69% ketepatan ujian. Perubahan signifikan dalam gelombang alfa dan beta diperhatikan semasa pendedahan RF. Penemuan ini menunjukkan bahawa data sintetik adalah alat berkesan bagi meningkatkan klasifikasi EEG dalam kajian kesan pendedahan RF.
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Rosli et al. (2026) studied Healthy (n=97). Synthetic data augmentation (CTGAN) vs. Original data only was evaluated on Feedforward Neural Network (FNN) testing accuracy for classifying male EEG signals during RF exposure. Augmenting EEG datasets with CTGAN-generated synthetic data improved the Feedforward Neural Network testing accuracy for classifying radiofrequency exposure in male subjects from 43% to 69%.
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