LookOmotiveThink About … Move Around

Team Supervisors

Prof. Dr. Mostafa Gad Dr. Howaida Abd El Fatah Dr. Safwat Hamad T.A Ramy Medhat

Members Mina Osama Fayek - Computer Science department Mona Mohamed - Scientific Computing department Mostafa Saeed - Scientific Computing department Nour Galal - Information Systems department Ossama Moussa - Scientific Computing department

Agenda Problem statement Objective Motivation Description

LookOmotive overview Previous approaches EEG signal processing Basic system architecture

Time Plan Tools, technologies and SWE

methodology References

Problem Statement For the time being handicaps need

human assistance to perform their everyday activities.

Objectives Help Handicaps to live independently

without others assistance. Learn about EEG signal processing to

detect cognitive states. Develop a generic SDK that can support

different applications (Ubiquitous environment).

Dealing with unfamiliar technologies and tools.

To be a good representative of Egyptian developers (Imagine Cup: Poland 2010)

-for more info…List location or contact for specification (or other related documents)

Motivation After considering the way handicaps

lead their lives, we have decided to integrate our technical background with science to provide them with means of making their lives better.

Description LookOmotive overview Previous approaches EEG signals Basic system architecture

LookOmotive Overview

LookOmotive overview LookOmotive is an assisting technology

that helps handicaps to control their wheelchair based on their cognitive state of mind .

LookOmotive user should think about an action (Forward, Backward, Right, Left or Stop).

Brian signals should be read, and then processed by LookOmotive.

LookOmotive should control the wheelchair according to the detected state.

Previous approaches Cap2Monitor [2009]: Computer

desktop control application based on detecting EEG status of specific muscular actions (Eye blinking & Focus).

EEG Signals

EEG Signals Introduction

Stands for electroencephalogram. The electroencephalogram (EEG) is a

recording of the electrical activity of the brain from the scalp.

The brain nerve cells in the brain produce signals that are called action potentials.

Measured in microvolts (µV) .

EEG Signals Signal detection

Electrodes: Small metal discs placed on the scalp in special positions.

These positions are set according to the International 10/20 System

EEG Signals Montages

The manner in which pairs of electrodes are connected to each amplifier of the EEG machine.

Differential amplifiers measure the voltage difference between the two signals at each of its inputs.

The resulting signal is amplified and then displayed as a channel of EEG activity.

Basic System Architecture

Basic System Architecture

EmotivEPOC SDK

Wheelchair

EPOC headset by Emotiv Systems

Basic System Architecture Why Not? Emotive SDKs

Developer/Research Standard Edition

Research Plus Edition

Cost US$ 500 US$ 2,500

Privileges

SDK detects certain cognitive, expressive and affective actions

Enables user to deal with raw brain data

Refusal •No science behind•Limited behaviors

•High cost

Basic System Architecture (Modified)

EPOC headset by Emotiv Systems

Wheelchair

EmotivEPOC SDK

LookOmotiveSDK

Wheelchair

Emulator

Service Config

UI

Basic System Architecture (Modified)

Initial Time Plan

Initial Time PlanOct. Nov. Dec. Jan. Feb. March April May June July

Research (EEG signal detection and processing )

Emulator design( 2 weeks)

Emulator implementation (1 month)

SDK design (2 weeks)

SDK implementation (4 ½ months )

Assembly (3 weeks)

Testing

Algorithms design (3 ½ months)

Documentation (3 weeks)

Tools

Tools and methodologies Tools & Technologies:

Microsoft .NET Framework (Visual Studio 2008)

Microsoft Office Live Workspace Operating Systems:

Windows XP Embedded (XPe) Hardware

x86 based development board Software development methodology:

Agile(Scrum)

Web Blog

http://lookomotive.wordpress.com/

News, updates and code samples ..

References Books

EEG signal processing - by Sanei, Saeid / Chambers, J. A.#v=onepage

EEG signal classification for brain computer interface application by

Y. P. A. Yong, N. J. Hurley, and G. C. M. Silvestre Computer Science, University College Dublin

Electroencephalogram: Basic principles, clinical applications and related fields

Determining user state and mental task demand from electroencephalographic data

Papers Introduction to EEG - by Smith, E.J ESTIMATING COGNITIVE STATE USING EEG SIGNALS - Tian

Lan, Andre Adami, Deniz Erdogmus, Misha Pavel EEG Signal Classification with Different Signal

Representations

Thank You!