Brain Computer Interfacing using Neuromorphic Computing

Brain Computer Interfacing using Neuromorphic Computing

Brain Computer Interfacing using Neuromorphic Computing

Neuromorphic Computing via PyCARL & Wyrm (Python) + Understanding Brain Computer Interfacing: 2 in 1 Course

Language: english

Note: 3.9/5 (41 notes) 9,374 students

Instructor(s): Junaid Zafar

Last update: 2022-07-08

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

 

Course content

  • Introduction to Brain Computer Interfacing (BCI)
    • BCI- An Introduction
  • Introduction to Deep Learning (AI)
    • Machine Learning & Deep Learning
  • Introduction to Brain Computer Interfacing
    • Brain Computer Interfacing- An Overview
  • Introduction to Spiking Neural Networks
    • Spiking Neural Networks for BCI
  • Fundamentals of Neuromorphic Computing
    • Neuromorphic Computing in BCI
  • Building an Artificial Brain using SpinNaker
    • BCI- Nueromorphic architectures for BCI
  • Deep Learning for Brain EEG Signals- BCI using PyWavelets
    • PyWavelets for BCI
  • Introduction to TinyML- Part I
    • TinyMl for BCI
  • Introduction to Tiny ML- Part II
    • TinyML
  • DeepC for Brain EEG Signals
    • DeepC for Brain Computer Interfacing
  • Neuromorphic Computing Mimics Human Brain
    • Neuromorphic Computing & BCIs
  • Neuromorphic Computing in Healthcare
    • Introduction to Quantum Neural Networks
  • How Human Brain is Interfaced with a Computer?
    • BCI Implementation
  • BrainNet- Brain to Brain Interfacing
    • BrainNet- Human Brain to Human Brain Interfacing
  • Introduction to RNNs
    • LSTMs- An Introduction
  • Deep Neural Optimizers for BCI
    • Deep Neural Optimizers
  • Brain Computer Interfaces & Neuromorphic Computing
    • BCI- Neuromorphic Computing
  • BCI- Spiking Neural Networks
    • BCI- Spiking Neural Networks
  • LOIHI2 & LAVA for Brain Computer Interfacing
    • LOIHI 2 for BCI
  • PyCARL & WYRM- Interfacing BCI with Python
    • PyCarl- Python Framework for BCI
  • BCI Augmentation using Spiking Neural Networks
    • BCI Augmentation
  • BCI- Software Platforms
    • BCI Softwares
    • BCIPy
  • Design & Implementation of BCI
    • Implementation of BCI
  • Implementation of EEG using BCI
    • EEG Motor Movements
  • BCI STACK Development Framework
    • BCI Stack
  • Deep Neural Networks for Implementing BCI
    • BCI implementation using Deep Neural Networks
  • Emotional Intelligence: Temperament Analysis for Regulation Emotions
    • Introduction
  • BCI for Stress & Anxiety Management
    • Things to do for an optimistic and positive outlook
    • Regulating Emotions Through Practice Exercises
    • How to avoid biases and Recurrent loops of Negative Thinking
  • Self Management Activity: Practical Exercise
    • How to improve Self Awareness?
    • How to avoid fears and develop positive thinking
    • Self Management Drills
  • Tapping the Potential of Positive Thinking
    • Accepting your Emotions
  • Happiness for Everyone through Personality Traits
    • Role of self determination in realizing positive thinking
    • Happiness for Everyone through Personality Traits
    • Unveiling the potential of positive thinking
  • Quantum DeepMind for BCI
    • Quantum DeepMind BCI

 

Brain Computer Interfacing using Neuromorphic ComputingBrain Computer Interfacing using Neuromorphic Computing

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