Brain Computer Interfacing Via Spiking Neuromorphic Networks

Spiking Neuromorphic Computing via PyCARL & Wyrm (Python): Understanding Brain Computer Interfacing (BCI) & Tiny ML

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

Overview

Section 1: Introduction to Brain Computer Interfacing (BCI)

Lecture 1 BCI- An Introduction

Section 2: Introduction to Deep Learning (AI)

Lecture 2 Machine Learning & Deep Learning

Section 3: Introduction to Brain Computer Interfacing

Lecture 3 Brain Computer Interfacing- An Overview

Section 4: Introduction to Spiking Neural Networks

Lecture 4 Spiking Neural Networks for BCI

Section 5: Fundamentals of Neuromorphic Computing

Lecture 5 Neuromorphic Computing in BCI

Section 6: Building an Artificial Brain using SpinNaker

Lecture 6 BCI- Nueromorphic architectures for BCI

Section 7: Deep Learning for Brain EEG Signals- BCI using PyWavelets

Lecture 7 PyWavelets for BCI

Section 8: Introduction to TinyML- Part I

Lecture 8 TinyMl for BCI

Section 9: Introduction to Tiny ML- Part II

Lecture 9 TinyML

Section 10: DeepC for Brain EEG Signals

Lecture 10 DeepC for Brain Computer Interfacing

Section 11: Neuromorphic Computing Mimics Human Brain

Lecture 11 Neuromorphic Computing & BCIs

Section 12: Neuromorphic Computing in Healthcare

Lecture 12 Introduction to Quantum Neural Networks

Section 13: How Human Brain is Interfaced with a Computer?

Lecture 13 BCI Implementation

Section 14: BrainNet- Brain to Brain Interfacing

Lecture 14 BrainNet- Human Brain to Human Brain Interfacing

Section 15: Introduction to RNNs

Lecture 15 LSTMs- An Introduction

Section 16: Deep Neural Optimizers for BCI

Lecture 16 Deep Neural Optimizers

Section 17: Brain Computer Interfaces & Neuromorphic Computing

Lecture 17 BCI- Neuromorphic Computing

Section 18: BCI- Spiking Neural Networks

Lecture 18 BCI- Spiking Neural Networks

Section 19: LOIHI2 & LAVA for Brain Computer Interfacing

Lecture 19 LOIHI 2 for BCI

Section 20: PyCARL & WYRM- Interfacing BCI with Python

Lecture 20 PyCarl- Python Framework for BCI

Section 21: BCI Augmentation using Spiking Neural Networks

Lecture 21 BCI Augmentation

Section 22: BCI- Software Platforms

Lecture 22 BCI Softwares

Lecture 23 BCIPy

Section 23: Design & Implementation of BCI

Lecture 24 Implementation of BCI

Section 24: Implementation of EEG using BCI

Lecture 25 EEG Motor Movements

Section 25: BCI STACK Development Framework

Lecture 26 BCI Stack

Section 26: Deep Neural Networks for Implementing BCI

Lecture 27 BCI implementation using Deep Neural Networks

Section 27: Emotional Intelligence: Temperament Analysis for Regulation Emotions

Lecture 28 Introduction

Section 28: BCI for Stress & Anxiety Management

Lecture 29 Things to do for an optimistic and positive outlook

Lecture 30 Regulating Emotions Through Practice Exercises

Lecture 31 How to avoid biases and Recurrent loops of Negative Thinking

Section 29: Self Management Activity: Practical Exercise

Lecture 32 How to improve Self Awareness?

Lecture 33 How to avoid fears and develop positive thinking

Lecture 34 Self Management Drills

Section 30: Tapping the Potential of Positive Thinking

Lecture 35 Accepting your Emotions

Section 31: Happiness for Everyone through Personality Traits

Lecture 36 Role of self determination in realizing positive thinking

Lecture 37 Happiness for Everyone through Personality Traits

Lecture 38 Unveiling the potential of positive thinking

Section 32: Quantum DeepMind for BCI

Lecture 39 Quantum DeepMind BCI

Beginners curious to learn about Brain Computer Interfacing using deep neural networks,Undergraduate & Graduate students aspire to kick start Human inspired Artificial Intelligence