Machine Learning for Neurotechnology

By Avni Bafna and Priyal Patel for Neurotech@Davis

Neurotechnology, a rapidly evolving field, aims to understand, monitor, and modulate brain function by integrating neuroscience and technology. One of the critical drivers behind this revolution is machine learning (ML), a subset of artificial intelligence (AI) that empowers computers to learn from data, make predictions, and uncover patterns without explicit programming. By applying ML to neurotechnology, researchers can decode brain signals, predict cognitive states, and create advanced brain-computer interfaces (BCIs), leading to breakthroughs in neuroprosthetics, mental health diagnostics, and cognitive enhancements.

In this section, we will explore the role of machine learning in neurotechnology, starting with the basics of machine learning, followed by key techniques like supervised and unsupervised learning, feature extraction, and various ML algorithms used in neurotechnology.