Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding

Understand

Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research.

• One of the goals for analyzing such data is hence to extract such 'shift-invariant' atoms.
• Even though some success has been reported with existing algorithms, they are limited in applicability due to their heuristic nature.
• Moreover, they are often vulnerable to artifacts and impulsive noise, which are typically present in raw neural recordings.