Classification of time-varying signals using time-frequency atoms

Extracting relevant features from signals is a key element in classification of signals, e.g., for the decomposition of electromyograms (EMG signals). We present an algorithm which uses time-frequency dictionaries and adaptively selects a small number of discriminant time-frequency atoms. Using our method, simulations show reduced misclassification rates compared to commonly-used linear classifiers.