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Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.

Abdulnasır Yıldız¹, Hasan Zan²

¹Department of Electrical and Electronics Engineering, Dicle University, Diyarbakır 21200, Turkey.

Diagnostics (Basel, Switzerland)

|March 14, 2026

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This study shows that the Short-Time Fourier Transform (STFT) combined with InceptionV3 deep learning model offers highly accurate dementia classification from EEG data, outperforming other time-frequency methods.

Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Dementia diagnosis is challenging, requiring accurate and timely detection.
Electroencephalography (EEG) provides a noninvasive method for assessing neurophysiological changes.
Automated EEG analysis frameworks are crucial for improving dementia diagnosis.

Purpose of the Study:

To systematically evaluate the impact of various time-frequency representations (TFRs) on dementia classification accuracy.
To assess TFR performance within a unified multi-channel EEG image fusion framework.
To compare different convolutional neural network (CNN) architectures for EEG-based dementia classification.

Main Methods:

EEG data from 88 subjects (Alzheimer's disease, frontotemporal dementia, controls) were analyzed.

Keywords:

deep learning dementia detection electroencephalography (EEG)multi-channel image fusion time–frequency representation

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Channel-wise EEG signals were transformed into time-frequency images using STFT, CWT, HHT, WVD, and CQT.

Fused image data from 19 EEG channels were classified using MobileNetV2, ResNet-50, and InceptionV3.

Main Results:

Classification performance varied significantly based on the TFR used.
The STFT representation with InceptionV3 achieved the highest accuracy (98.8% random split, 84.3% subject-wise split).
Constant-Q Transform (CQT) showed competitive results, while HHT and WVD were less effective.

Conclusions:

The choice of TFR significantly impacts EEG-based dementia classification accuracy.
Structured multi-channel fusion and systematic TFR evaluation are vital for robust diagnostic frameworks.
The findings provide a foundation for developing interpretable and reliable EEG diagnostic tools for dementia.