mai mohamed project team brain computer interface mohamed omar mohamed sami nada mohamed ahmed...

Mai Mohamed Project TeamProject Team

Brain Computer Interface

Mohamed Omar

Mohamed Sami

Nada Mohamed

Ahmed Mamdoh

http://bci2.k-space.org

Brain Computer

Interface

BCI

Brain Computer

Interface

BCISupervisorsProf.Dr Abu Bakr M. Youssef Assistant Prof.Dr Yasser M.Kadah

SupervisorsProf.Dr Abu Bakr M. Youssef Assistant Prof.Dr Yasser M.Kadahhttp://bci2.k-space.org

Motivation for BCI ResearchMotivation for BCI ResearchThere are , more than 200,000 patients live with the motor sequelae of serious injury.

Locked-in SyndromeNeurological diseases may lead to paralysis of the entire motor system .Unable to use their muscles and therefore cannot communicate their needs, wishes, and emotions.


Reason of BCIReason of BCI

• Allow a user to communicate with a computer through his Brain

• The user can think and the computer recognizes what he thought about.

• This is what we call a Brain-Computer Interface (BCI) [or Brain-Machine Interface (BMI)].


The DreamThe Dream

•People always think about controlling environments from their mind

•Anyone wish if he could read the people thoughts and know what they are thinking of him

•Some people want to store their dreams and record it while they sleeping


Dream vs. RealityDream vs. Reality• Dream BCI

– Think to whatever you want– Without recognition errors – Whenever you want

• Physiological problems– No thought sensor– Partial brain knowledge– Noisy signals

• Solutions in the BCI community (reality)– Limited thought– Limited recognition accuracy


BCI communityBCI community

2 4

48

127

0

20

40

60

80

100

120

140

1985-1990 1991-1995 1996-2000 2001-2004

SCI paper

About 60 research groupsAbout 300 researchersIncreasing published papers


Our GoalsOur Goals

1. Recording Brain Signal Using EEG electrodes.1. Recording Brain Signal Using EEG electrodes.

2. Isolation between subject and electronic circuit2. Isolation between subject and electronic circuit

3. Designing Data Acquisition System3. Designing Data Acquisition System

4. Signal Selection 4. Signal Selection

5. Interfacing with Computer by Soundcard5. Interfacing with Computer by Soundcard

6. Implementing real time analysis Classification data6. Implementing real time analysis Classification data

BCI CategoriesBCI Categories

• Invasive and Non-Invasive BCIs

• Online and Offline BCIs

• Imaginary and Mental Tasks


General schemeGeneral scheme

Controlinterface

Application

Bio-sensor

Pre-processing

Feature extraction

Classification

Electrical activity

The brainOn the computer

Mental

state

High levelcommands

1. Data Acquisition 2. BCI System

3. Online Feedback

Feedback


Medical IntroductionMedical Introduction

Nervous System Nervous System

Nervous System Nervous System

PNS PNS

CNS CNS

Brain

SpinalCord

CranialNerves

SpinalNerves

Sensory

Motor


Human Brain Human Brain


EEGEEG

Electroencephalography or EEG is the measurement of neural activity within the brain.

EEG has been used to detect low oxygen and high carbon dioxide levels.

A clinical use of EEG is in the diagnosis of epilepsy.


EEG SignalEEG Signal


EEG Wave BandEEG Wave Band

Alpha Beta Delta Theta

Frequency 8-13 Hz 13-30 Hz 0.5-4 Hz 4-8 Hz

Occupation occipital

parietal and frontal lobes. ______

Conditionawake person

_____ Sleeping _____

Age ______ ______infants&adults

children and sleeping adults

EEG Lead SystemEEG Lead System


Data Acquisition Data Acquisition


OverviewOverview

Pre Amplifier

Pre Amplifier

Pre Amplifier

Pre Amplifier

Pre Amplifier

Pre Amplifier

MUX

Latch

Parallel Port

Gain Amplifier LPF Sound Card

ElectrodeIsolation

ElectrodeIsolation

ElectrodeIsolation

ElectrodeIsolation

ElectrodeIsolation

ElectrodeIsolation

Matlab Workspace


Biopotential SensorsBiopotential Sensors

Electrodes are Biopotential sensor.

There are different types of electrodes:

1. Gold electrode.2. Silver electrode.


IsolationIsolationMedical procedures usually expose the patient

to more hazard than at home or workplace.

Our main goal is to break ground loop .

We decide to do that by low cast and effective way by using:

1.Isolation transformer as power isolation.2.Opto-Couplers as signal isolation.


IsolationIsolation

In our design we used the PC817 due to:Its low turn-on and off time and high.Isolation voltage between input.

Continue


Instrumentation AmplifierInstrumentation AmplifierAmplifying differential input

There are two stage of signal amplification:1.Pre-Amplification2.Gain-Amplification

We used AD620 according to many better features on it:

• Lower cost • High accuracy• Low noise• High Gain Ability


AD620 SchematicAD620 Schematic


Signal SelectionSignal SelectionMultiplexer:

Select data from two or more data sources into a single channel.

There are two types of multiplexers:•Analog Multiplexer.•Digital Multiplexer.

we used Analog Multiplexer and we choose M54HC4051 IC

Some features of M54HC4051:• Low power dissipation• Fast switching• High noise immunity• Wide analog input voltage range


M54HC4051 SchematicM54HC4051 Schematic


LatchLatch

Change output state only in response to data input

Transfer data from parallel port to MUX and holding it using LE (latch enable).

In our design SN74LS373 As latch IC.


SN74LS373 SchematicSN74LS373 Schematic


Signal FilteringSignal FilteringA low-pass filter is a Filter that passes low frequensy Component well, reduces frequencies higher than the cutoff frequency.

It is sometimes called a high-cut filter, or treble cut filter.

We use active 2nd order low pass filter we used UA741 IC.

LPF SchematicLPF Schematic


Parallel PortParallel PortThe Parallel Port is the most commonly used port for interfacing home made projects

•Hardware Properties

8 output pins accessed via the DATA Port 5 input pins (one inverted) accessed via the STATUS Port 4 output pins (three inverted) accessed via the CONTROL Port The remaining 8 pins are grounded

•Why Parallel Port ?

Easy Implementation and InstallationAllow Full Software Control without- need any Counters &Clock to Switch between ChannelsAbility of Communication withMatlab


Sound CardSound Card

A sound card is a Computer PCI Card that can input and output Sound under control of computer programs General characteristics 1-Sound Chip2- multi-channel Dacs & A/D 3-ROM or Flash memory

Color Function

Lime gree

n

Analog line level output for the main stereo signal (front speakers or headphones).

Pink Analog Microphone input.

Light blue

Analog Line level input.


Continue

Sound CardInternal Block

Sound CardInternal Block

Why we choose Sound CardWhy we choose Sound Card

• Fixed and Low Cost Acquisition Card• Easy in Implementation and installation• Ability to Convert from Analog to Digital with very

high accuracy and vise versa• Easy Communication with Matlab• Ability to detect low Frequencies• Sampling Data in wide rang (8000 to 44100)• Better than designing new Interfacing System and this

System in Situation to not work because of hardware troubleshooting


Acquiring Data with a Sound CardAcquiring Data with a Sound Card


Data Acquisition ProgramsData Acquisition Programs

OnlineSignal with

Filtering

EachChannelOnline

Plotting

DrawingWith

Selection

OnlineClassifier

1st

Release2nd

Release3rd

Release

4th

Release


1st Release1st Release


2nd Release2nd Release


3rd Release3rd Release


4th Release4th Release

imagination of right hand movement

imagination of left hand movement


Analytic methodsAnalytic methods

Signal preprocessing

Signal preprocessing

Feature extraction

Feature extraction

Statistical classification

Statistical classification

The process of EEG signal analysis and classification consists of the Following three steps:The process of EEG signal analysis and classification consists of the Following three steps:


Signal PreprocessingSignal Preprocessing

Backgroundbrain

activityPhysiologic

noiseEnvironmental

noise

Measuredsignal

Mentaltask

• Power line 50/60 Hz• Electrode contact

• Eye movements• Other movements

?

Experiment protocol

Power line

Heart rateSubject

Eye blink

Electrode contact

Noisy signal

Start

Type of work

Work on exist dataset

Offline

Online

Record EEG signal

Read dataset

Feature extraction

hypothesis test

Feature available

F

Classification

TTest next feature

Test classifier

feature extraction

Decision

classification

Record our dataset

Feature extraction

Make hypothesis test

Classification

Visual O/P

Red Green


Offline DatasetBCI Competition 2003 Data Set Ia: ‹self-regulation of SCPs› provided by University of

Tuebingen,Germany, Dept. of Computer Engineering (Prof. Rosenstiel)

Offline DatasetBCI Competition 2003 Data Set Ia: ‹self-regulation of SCPs› provided by University of

Tuebingen,Germany, Dept. of Computer Engineering (Prof. Rosenstiel)

Datasets were taken from a healthy subject he was asked to move a cursor up and down on a computer screen.

Data6 EEG electrodes are used referenced to the vertex electrode Cz •Channel 1: A1-Cz (A1 = left mastoid) •Channel 2: A2-Cz (right mastoid)•Channel 3: 2 cm frontal of C3 •Channel 4: 2 cm parietal of C3•Channel 5: 2 cm frontal of C4•Channel 6: 2 cm parietal of C4Sampling rate of 256 Hz.

Trial structure overviewconsisted of three phases1-s rest phase,1.5-s cue presentation phase and 3.5-s feedback phase.


Offline DatasetOffline Dataset

During every trial, the task was visually presented by a highlighted goal at the top or bottom of the screen to indicate negativity or positively from second 0.5 until the end of the trial. The visual feedback was presented from second 2 to second 5.5. Only this 3.5 second interval of every trial is provided for training and testing.

Trails separated into training set (268 trials) which is 2-D Matrices 135x5377 and 133x5377 testing set The test set (293 trials). Every line of a matrix contains the data of one trial. The first column codes the class of the trial (0/1).

•NoteFor our implementation we constructed the test set from the train set. That was done by selecting 100 trails from class 0 and 100 trails from class 1.

Continue


Offline DatasetOffline DatasetApproachWe used MATLAB (release 13) for analysis. We separated the channels of each class to be 135x896 matrix for channels in class 0 and 133x896 matrix for class 1 channels.For each EEG channel, we plotted the time-domain and frequency-domain averages across trials for each class.

NoteIn our online BCI approach, we constructed our own

dataset which consist of training set & testing set.The training set was used to tune the parameters of

the classification algorithm.We also applied all the pre-processing techniques as in

the offline work.

Continue


Feature ExtractionFeature Extraction

Steps of feature extraction

Choosing feature

Features Vector Form


Choosing FeaturesChoosing Features

Time Domain Features

mean

Variance


Frequency domain featuresFrequency domain featuresShort-Time Fourier Transform–First we transform all signals to frequency domain by (FFT).–Then we get mean & variance in frequency domain .–calculate the amplitudes at 20 Hz.

Welch methodEstimate the power spectral density (PSD) of a signal using Welch is done using Pwelch Matlab function

Continue

Form features vectorForm features vector

Std1Std1Std2Std2Std3Std3Std4Std4

::::::::::::::::::

Channel 1

Signal 1Signal2Signal3Signal4

::::

: : :

feature vector

Class 0

Class 1

Class 0

Class 1


Form Features VectorForm Features Vector

Ch1 Feature Vector

Class 0

Class 1

mean2

mean1

mean3

mean4

Var1

Var2

Var3

Var4

.

.

.

.

Continue


Multi dimension feature vectorMulti dimension feature vector

Channel 1

Channel 2

Channel 5

Channel 6


Hypothesis TestHypothesis Test

Perform Hypothesis testing for the difference in means of two samples.


[H, P, Ci]=ttest2(X,Y)

H=0 no significance

H=1 significance

Signal Classification TechniquesSignal Classification Techniques

Classifier

Minimum Minimum DistanceDistance

BayesBayes K-NNK-NN


Classifier inputClassifier input

Train feature vector Test feature vector

Class 0

Class 1

Class 0

Class 1

Minimum Distance ClassifierMinimum Distance ClassifierMinimum Distance ClassifierMinimum Distance ClassifierAlgorithm 1. Group the design set into (n) class 2. Estimate the sample mean for each class.3. A test sample is classified by assigning it to the class

which has the nearest mean vector.4. Error rate is estimated by the percentage of

misclassified samples


Bayes ClassifierBayes ClassifierAlgorithm Compute Gaussian distribution of each class (p.d.f) Compute probabilities of sample (a)F( a Є f0) & F( a Є f1)


K-Nearest Neighbor (KNN)K-Nearest Neighbor (KNN)Algorithm 1. Obtain distances between

test sample and all samples in the design set

2. Sort obtained distance values in ascending ordered array.

3. Assigns the test sample to the majority class in the subset.

4. Error rate is estimated by the percentage of misclassified samples

ResultsResults

Dataset Ia results

Our dataset results


Dataset Ia Best results Dataset Ia Best results

FFT feature (amplitude of 20 HZ)

KNN k=3 Accuracy Error

channel 3 80% 20%

Pwelch feature


channel 4 78% 22%


Our Dataset Best ResultsOur Dataset Best Results

FFT feature (amplitude of 20 HZ)


channel 3 54% 46%


channel 4 58% 42%

Pwelch feature


BCI challengeBCI challenge

Information transfer rate.High error rate.Autonomy.Cognitive load.


Conclusion & FutureConclusion & Future

• In our project we built a simple BCI ,which separated between left and right hand movement

• System worked on online & offline data set• Online data pass through different stages:

FiltrationAmplificationInterfacing with computer using soundcardAnalysis and classify


Conclusion & FutureConclusion & Future

Completely paralyzed patients can use a BCI to realize a spelling system (virtual keyboard) to install a new non muscular communication channel.

•In the future: It will be used by total normal people to perform simple activities Spread commercially in the field of video gamesIn military


Online DemoOnline Demo

Thank YouThank You

mai mohamed project team brain computer interface mohamed omar mohamed sami nada mohamed ahmed...

Documents

braincomputer interface

human brain http

brain signal

orgdata acquisition

eeg electrodes

brainmachine interface

entire motor system

bci researchthere