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Encompassing performance indicators 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.
The dataset is in XLS format and is very small (5.5 KB), containing likely summary performance indicators, not raw time-series EEG signals. Users should reference the associated paper for full methodological context.