Cross-wavelet transform aided focal and non-focal electroencephalography signal classification employing deep feature extraction

Epileptic Seizure Prediction from Eigen-Wavelet Multivariate Self-Similarity Analysis of Multi-Channel EEG Signals

“…Related works. Seizure prediction is an important research topic, often investigated using tools such as synchronization and functional connectivity [7], phase coherence [8], power spectral density [9], [10], cross-power spectral density [11] or power of the wavelet coefficients [12] in standard frequency bands, autoregressive models, or more recently deep learning frameworks [13]- [15]. Moreover, feature extraction for seizure prediction often involves channel selection to decrease computational complexity or reduce overfitting (see [16] for a review).…”