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

INTRODUCTION? Problem Statement

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.

INTRODUCTION? WHY THIS PROJECT?

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.

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.

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

INTRODUCTION? Project Scope

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

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

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.

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

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

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.

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

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

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

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

System Model

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

Convolution Neural Network

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

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.

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

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

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

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 )

THANK YOU

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

Result and Analysis Internet of Things Website for IOT Control

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

Result and Analysis Headset construction 3D Printed Headset Frame

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