Mentor Based Artificial Intelligence in Neuroscience and Neurotechnology

About This Course

This 3-week course delves into the fascinating intersection of AI and neuroscience, focusing on the application of neural networks, predictive modeling for neurological disorders, and brain-computer interfaces (BCIs). Participants will gain hands-on experience with AI tools for neural data analysis, disorder prediction, and BCI development, while exploring the ethical implications of AI in clinical settings. Through interactive sessions, real-world case studies, and practical demonstrations, you’ll learn how AI is reshaping the landscape of neurotechnology.

Aim

To equip participants with the skills and knowledge to apply AI in the fields of neuroscience and neurotechnology, focusing on neural networks, predictive modeling for neurological disorders, and the development of brain-computer interfaces (BCIs). This course will provide hands-on experience and ethical discussions surrounding the clinical use of AI.

Course Structure

Module 1 — Foundations of Neural Networks and AI in Neuroscience

• Introduction to Intelligent Machines: What is AI, and how does it relate to neuroscience?

• The relationship between biological neural networks and the development of artificial neural networks (ANNs)

• How ANNs are helping us understand neural behaviors, including cognition and sensory processing

• AI in neuroscience: From foundational theories to real-world applications

• Open Discussion & Q&A: Exploring AI’s role in advancing our understanding of the brain and neural processes

Module 2 — Prediction of Neurological Disorders using Non-electrophysiological Signals

• Gait analysis in Ataxia: Understanding this neurological disorder through motion data

• Using AI/ML techniques for solving the Ataxia classification and regression problems

• Hands-on demo: Using a transformer-based neural network for Ataxia prediction

• Exploring how non-electrophysiological signals can help in predicting movement disorders

• Open Discussion & Q&A: Applying AI for early diagnosis and prediction in neurological conditions

Module 3 — Prediction of Neuropsychiatric Disorders using Electrophysiological Signals

• EEG recording techniques: Basics of electroencephalography (EEG) for brain activity monitoring

• Introduction to Quantitative EEG (qEEG) and its importance in disease prediction

• Data preprocessing pipelines: Artifact removal, filtering, and Independent Component Analysis (ICA)

• Machine learning applications: Extracting quantitative EEG markers for neurological disease prediction

• Open Discussion & Q&A: Exploring the challenges and opportunities of AI in neuropsychiatric disorders

Who Should Enrol?

This course is designed for researchers, healthcare professionals, engineers, and students in neuroscience, neurotechnology, computer science, AI, and related fields.

• Ideal for those interested in the application of AI to brain health, cognitive disorders, and neurotechnology

• Basic knowledge of AI and machine learning is recommended but not required

• Healthcare professionals looking to integrate AI into clinical applications for neurological and psychiatric disorders