Mentor Based Artificial Intelligence in Neuroscience and Neurotechnology

Introduction to the Course

Course Objectives

• Understand the fundamental concepts of artificial intelligence and its implementation in neuroscience and neurotechnology.
• Learn how machine learning algorithms can be implemented to analyze neuroimaging data, neural signals, and other brain-related data.
• Get practical experience with AI models implemented in brain-computer interfaces, neuroprosthetics, and cognitive enhancement.
• Acquire expertise in implementing AI techniques for neural pattern recognition, brain activity mapping, and neural signal decoding.
• Investigate the ethical issues and challenges involved in implementing AI in neuroscience and neurotechnology, such as data privacy, neural rights, and brain data security.
• Acquire the ability to implement AI models on real-world neuroscience and neurotechnology data to improve research and technological development.

What Will You Learn (Modules)

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

Who Should Take This Course?

This course is ideal for:

• Neuroscientists, neurologists, and neurotechnologists looking to incorporate AI into their research and applications.

• AI researchers and data scientists interested in the application of machine learning to brain data and neurotechnology.

• Engineers and bioengineers working in the field of neurotechnology, BCIs, and neuroprosthetics.

• Students in neuroscience, bioengineering, or cognitive science who want to specialize in the integration of AI and neurotechnology.

Job Opportunities

• After completing this course, learners can pursue roles such as:
• Neurotechnology Engineer (AI Integration)
• Brain-Computer Interface (BCI) Specialist
• AI in Neuroscience Researcher
• Neuroprosthetics Design Engineer
• Data Scientist (Neuroscience and Neurotechnology)

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Expert-led training: Learn from instructors with real-world experience in applying skills to industry and research problems.
• Practical & hands-on approach: Build skills through guided activities, templates, and task-based learning you can apply immediately.
• Industry-aligned curriculum: Course content is designed around current tools, workflows, and expectations from employers.
• Portfolio-ready outcomes: Create outputs you can showcase in interviews, academic profiles, proposals, or real work.
• Learner support: Get structured guidance, clear learning paths, and support to stay consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• Expertise in AI methods for neuroimaging data and neural signal analysis
• Practical skills in AI-based systems for brain-computer interfaces (BCIs) and neuroprosthetics
• Good knowledge of data-driven neuroscience, such as neural pattern recognition and brain mapping
• Knowledge of the ethical issues involved in using AI in neuroscience
• Employable skills in AI-based neurotechnology design and brain data analysis