Brain Computer Interfacing via spiking neuromorphic networks
What you'll learn
- Brain Computer Interfacing using spiking neural networks
- Quantum spiking neural networks for re-wiring human brain
- Drills/ Exercises on Brain Computer Interfacing using EEG Signals
- How Brain Computer Interfacing is used for neuro-rehabilitation
- Recurrent Neural Networks & LSTMs for Brain Computer Interfacing
- Brain Computer Interfacing for Medical Imaging (Healthcare IT)
- Brain Computer Interfacing- Human Brain on a Chip
- Neuromorphic computing and Spiking Networks
Requirements
- No requirements
Description
Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures such as the Spiking Neural Network (SNN). This exciting course introduces you to the next generation of Machine Learning. You would be able to learn about the fundamentals of Spiking Neural Networks and Brain-Computer Interfacing (BCI).
This course has the rigour enough to enable you not only to understand BCI but its implementation in spiking neural networks and to apply these concepts to Brain Healthcare (IT) even on edge machines using Tiny ML.
TinyML is a field of study in Machine Learning and Embedded Systems that explores the types of models you can run on small, low-powered devices like microcontrollers. It enables low-latency, low power and low bandwidth model inference at edge devices. While a standard consumer CPUs consume between 65 watts and 85 watts and standard consumer GPU consumes anywhere between 200 watts to 500 watts, a typical microcontroller consumes power in the order of milliwatts or microwatts.
That is around a thousand times less power consumption.
The course contents includes;
1. Introduction to Machine Learning, Deep Learning, and Artificial Intelligence.
2. How Quantum Computing is fuelling AI Healthcare Systems including BCIs.
3. Introduction to Recurrent Neural Networks.
4. Introduction to LSTMs.
5. Introduction to Brain-Computer Interfaces.
6. How BCI is used for neuro- rehabilitation.
7. Brain-Computer Interfaces for Stress and Mood Regulation.
8. Brain-Computer Interfaces for Motor Imagery & EEG Signals.
9. Brain Implants using Brain-Computer Interfacing.
10. BCI for Medical Imaging.
11. Introduction to "Brain- on- a Chip.
12. Neuromorphic Computing for Brain Computer Interfacing.
13. Introduction to Tiny ML.
14. Tiny ML for Real Time Applications
Who this course is for:
- Beginners curious to learn about Brain Computer Interfacing using deep neural networks
- Undergraduate & Graduate students aspire to kick start Human inspired Artificial Intelligence
Instructor
![Junaid Zafar](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.0 Instructor Rating
- 441 Reviews
- 43,546 Students
- 9 Courses
Prof. Dr. Engr. Junaid Zafar is currently working as Chairperson in Department of Electrical and Computer Engineering, Government College University, Lahore. He is also Director, Office of Research, innovation and Commercialization. He has completed his PhD in Electrical and Electronics Engineering, The University of Manchester University, UK, and BSc in Electrical Engineering from U.E.T Lahore. He is Academic visitor to the University of Cambridge, UK, MMU, UK and National University of Ireland. He remained Dual Degree programme coordinator at the Lancaster University, UK. Dr. Engr. Junaid Zafar received Roll of Honors for National Education Commission and Outstanding Teacher/ Researcher Awards from the Higher Education Commission, Pakistan. He is leading the macine learning and Artificial Intelligence centre with GC University, Lahore. He is member of Universal Association of Electronics & Computer Engineers, International Association of Computer Science & Information, and member of International Association of Engineers, IAENG Society of Artificial Intelligence, IAENG Society of Electrical Engineering, Science & Engineering Institute, IAENG Society of Imaging Engineering, Institute of Research Engineers & Doctors, and IAENG Society of Wireless Networks. He is member of editorial board in Journal of Future Technologies & Communications, Technical Programme committee, Frontiers of Information & Technologies, and Technical Programme Committee, Multi- Conference on Sciences & Technology. He is also serving as reviewer for IEEE Transactions on Microwave Theory & Techniques, IEEE Transactions on Antennas, IEEE Antenna & Wireless Propagation Letters, IEEE Transactions on Plasma Science, IEEE Transactions on Magnetics, International Journal of Electronics, and IET Antennas & Radio- wave Propagation. He has so far taught over twenty diffrent online courses based on outcome based student oriented models. He has also supervised more than 100 Masters/ MPhil thesis. He has published over 50 high impact factor publications and presented his work at several national and international renowned platforms.