1. Home Automation Enhancement with EEG Based Headset (Orpheus)
Ankit Shrestha
071BCT505
Institute of Engineering
Pulchowk, Lalitpur
Bikram Adhikari
071BCT549
Institute of Engineering
Pulchowk, Lalitpur
Shailesh Mishra
071BCT538
Institute of Engineering
Pulchowk, Lalitpur
Suyog Singh
071BCT547
Institute of Engineering
Pulchowk, Lalitpur

2. INTRODUCTION?
Problem Statement

3. Problem Statement
People with visual and locomotive imparities have problems with their day - to - day activities.
Some of times, leads to harm in self confidence
Inefficiency due to dependency on others.

4. INTRODUCTION?
WHY THIS PROJECT?

5. GENERAL
Introducing Brainwave as one of the most efficient input technology.
Brainwave can be used for helping people with various types of disabilities (Visually impaired).
An interesting field of technology just in its beginning and major possibilities for future.

6. HOME AUTOMATION
Voice command is the most used technology for home automation.
It can’t be used in noisy environments.
The system accuracy is high in closed environment. But actual accuracy in day to day life is very low. Voice system is irritating and ineffective.

7. EEG for Impaired
Independent control over the home operations like switching on/ off of home appliances.
Boost in self morale.

8. INTRODUCTION?
Project Scope

9. SCOPE STATEMENT
In this project, a hardware software interface to automate household works (lights, fan, etc.) using brain waves.

10. SCOPE DESCRIPTION An Intelligent application with a friendly UI
Simple Home Automation Model

11. SCOPE LIMITATION
This is only a trained intelligent machine and not magic. Just thinking anything will not work.
Budgetary Limitations are considered and majority of the project will only be a simple model.

12. IMPACT Life Changing for Disabled People
Change the interaction method to technology

13. CHALLENGES IN NEPAL Fear of the Unknown Difficulty in Use
Lack of Hardware Components

14. Internet of Things(IoT)
Network of physical devices, vehicles, home appliances and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these objects to connect and exchange data.

15. Internet of Things(IoT)
Ecosystem of connected devices that are available through the internet.

16. IoT Components Sensors : BCI Headset
Processing Unit : Raspberry Pi / Remote Server
Interfacing Unit : Arduino / Relay

17. Why Raspberry Pi? Size of a credit card Easy to install to a network
Economic

18. Hardware Cost* IoT : < $50 BCI Ganglion Board: $200
3D Printed Hardware cost < $50
*Total manufacturing cost to increase by more than 25% when in mass production

19. System Model

20. Convolution Neural Network
CNN are a specialized kind of neural network for processing data that has a known, grid-like topology. Eg. Image, videos.

21. Convolution Neural Network

22. Recursive Neural Network
Recurrent neural networks or RNNs are a family of neural networks for processing sequential data.

23. Recursive Neural Network
x_t is the input at time step t
s_t is the hidden state at time step t
o_t is the output at step t.

24. Recursive Neural Network
Suffers from problem of vanishing gradient or exploding gradient

25. LSTM Network LSTM Network consists of LSTM cell
Solves issues with Vanishing gradients or Exploding Gradients

26. RNN vs CNN
CNN learns and recognizes pattern in the grid systems, eg images recognition
RNN learns and recognizes pattern in the sequence or systems varying in function of times. Eg. Image captioning

27. Problems in Existing EEG Solutions
None in Local Level
Only used for entertainment or experimental purposes.
Risky startup idea.
Costly. (Emotiv-500$ 5 channel; Muse: The Brain-sensing Headband, $299, has 4 channels )

28. THANK YOU

29. Result and Analysis Neural network training:
The graph shows the training and validation accuracy obtained on training the model over 50 epochs.

30. Result and Analysis
Internet of Things
Website for IOT Control

31. Result and Analysis Internet of Thing Device 1 turned on
Device 1 turned off
Device 1 turned on

32. Result and Analysis
Headset construction
3D Printed Headset Frame

33. FUTURE ENHANCEMENTS System Integration
Implementation of Music Controller
Improvement in the Model Architecture
Mobile Implementation of the System
Scaling the Project to Deployment Level