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HybridEEGNet: Combining Spatial Attention and Temporal Convolutions with Transformer Encoders for Automated Alzheimer's Disease Detection from EEG Signals
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HybridEEGNet: Combining Spatial Attention and Temporal Convolutions with Transformer Encoders for Automated Alzheimer's Disease Detection from EEG Signals
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting over 55 million people worldwide, with early detection being crucial for effective intervention and care planning. Electroencephalography (EEG) offers a non-invasive, cost-effective, and widely accessible approach f...
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