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A collection of performance metrics from a study comparing continuous wavelet transform (CWT) and short-time Fourier transform (STFT) feature extraction methods for epileptic EEG signal detection. The study evaluated three neural network models—EEGNet, AlexNet, and Shallow ConvNet—with targeted optimizations like Focal Loss and dynamic data augmentation. Results indicate the CWT-based method outperformed STFT, with the CWT+Shallow ConvNet combination showing optimal overall performance.
Data is in XLS format and is only 5.5 KB, indicating it is a small set of summary metrics or results, not raw EEG time-series data. License is CC BY 4.0.