Time Series Classification with Deterministic Learning

Constructed an RBF Neural Network for EEG classification, emphasizing feature extraction and strategic handling of imbalanced data; the model was trained in over 2,000 time-series samples, ensuring consistent predictions.
Analyzed the wavelet decompositions using the Discrete Wavelet Transform to reconstruct the EEG data and fit into the Entropy measures for feature extraction, to train the Neural Network, and increase efficiency to 72% from 50%.