Topic: temporal features

EEG has several applications in neuroscience, neural engineering, and diagnosis of neurological disorders. It records the electrical activity of the brain tissues resulting from the interaction of distinct neuronal populations. Various methods have been developed for the automatic classification of EEG signals. These algorithms operate by acquiring unique and non-redundant features from EEG data. However, a large portion of the proposed algorithms usually use temporal features while ignoring the dense spatial network structure generating the EEG. We propose a classification pipeline that utili

Auditory decoding of temporal features is a complex process in which vocalizations such as birdsongs are segmented into distinct auditory units, a crucial step in transforming sound into meaning. The songbird auditory system offers a valuable model for studying the neural representation of complex sounds. The hierarchical structure of the Zebra Finches' song makes it ideal for in-depth analysis of auditory decoding. We use stacked Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to decode the amplitude envelope and the time-locked envelope features of zebra finch songs. To a