Neural signal processing is the study of how electrical activity generated by neurons can be measured, analyzed, and interpreted to understand brain function and develop neural technologies. It lies at the heart of brain–computer interfaces, prosthetic control systems, and cognitive-state monitoring tools.

The Neural Signal Processing session emphasizes the transformation of raw neural data into meaningful information through advanced computational techniques. Researchers focus on filtering, decoding, and modeling signals obtained from EEG, MEG, LFP, and multi-unit recordings to explore how neuronal populations encode sensory and motor information.

At the Neural Signal Processing Conference, experts from neuroscience, electrical engineering, and data science present cutting-edge tools for signal acquisition, real-time processing, and machine-learning-based pattern recognition. Topics include adaptive filtering, spike-sorting algorithms, neural decoding models, and hardware innovations for implantable devices.

This session provides a multidisciplinary overview of how signal processing supports diagnosis, neuroprosthetics, and cognitive research. Attendees will explore applications in seizure prediction, movement restoration, and closed-loop neuromodulation.

Advances in Neuroinformatics and computational modeling are transforming how researchers manage and interpret vast datasets, paving the way for next-generation neuroengineering.