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EEG Brain Signal Classification for BCI
Applications http://eegclassifyandrecognize.blogspot.com/
Project MembersSupervisors
•Pro.Dr Mostafa Gad-Haqq•Pro.Dr Tareq Gharib•Dr.Howida Shded
Assistants•T.A Manal Tantawy
Team Members• Ahmed Khaled Abd El-glil (Information Systems)• Ahmed Mohamed Ahmed Mahany (Computer Systems)• Islam Ahmed Hamed Elgarhy (Computer Systems)• Kamal Ashraf Kamal El-deen (Information Systems)• Mohammed Saeed Ibrahim (Scientific Computing)
Agenda
Project Overview
• Objectives
• Problem Statement
EEG Signal Overview
System Architecture
Methodology
Past, Present, Future Work
Time Plan
References
Project Overview(Objectives)
Develop a generic EEG Classification that can be used in different brain computer interface applications.
EEG Signal Overview
An electroencephalogram is a measure of the brain's voltage changes as detected from scalp electrodes.
Measured in microvolt (µV) .
EEG Signal Overview(Cont.)
DELTA
• Up to 4
• These occur in deep sleep
• Childhood and in serious
organic brain diseases
THETA
• 4 – 7
• Move of Hand
• Idling
• These occur during childhood
ALPHA
• 8-12
• relaxed/reflecting
• closing the eyes
BETA
• 12-30
• Thinking
• Alert & working
GAMMA
• 30-100
• Short term memory of (sound , tactile,…..)
System Architecture
EEG Signal Pre-
processing
Feature Extraction
EEG Signal acquisition
Classification
EEG Signal Post-Process
Feature Selection
The whole classification system contains four parts :
• Preprocessing
• Feature Extraction and Features Selection ( Dimensionality Reduction)
• Classification
• Post processing
System Architecture(cont.)
Methodology
Independent Component Analysis(ICA) for Feature extraction.
Convolution Neural Network for classification.
Preprocessing
Filter out Noise And Remove The Artifacts
• Removing eye blinks and muscular movements
Feature Extraction & Selection
Power Spectral Density
• Temporal information from a window of data is extracted and then processed using a static classifier.
Spatial information alone is indeed powerful enough to produce state-of-the-art performance
• Independents Components Analysis (EXPLAIN NEXT)
Feature Extraction & Selection
The goal of feature subspace projections is to improve classifier robustness by reducing data dimensionality in order to facilitate better generalization, as well as reducing the learning and operating complexity of the classifiers.
Independents Components Analysis
Definition
• Mixed Signals in Matrix Notation
n
i
is1
iasAx
Signalt Independen i ~
Matrix ngMultiplexi ~
Signal dMultiplexe~
this
A
xj
Find W using the ICA Algorithm
ICA Block Diagram (2 Signals)
ssIsA)(W
s)(AWxWs
ˆ
Signal #1 Signal #2
MultiplexedSignal #2
MultiplexedSignal #1
a11
a12 a21
a22
Independents Components Analysis
ICASensor #3
Sensor #4
Sensor #2
Sensor #1
Feature #3
Feature #4
Feature #2
Feature #1
Independents Components Analysis
Classification
Neural Networks
• An information processing paradigm that is inspired by the way biological nervous systems, such as the brain.
• It Composed of large number of neurons to solve a specific problem.
How the Human Brain Learns?
Neuron collects signals from others through dendrites.
The neuron sends out spikes of electrical activity through a long axon.
Synapse converts the activity from the axon into electrical effects.
NeuralNetworks (cont.)
General function can be formalized as
X1W1 + X2W2 + X3W3 + ... > T
Xp : Input Pattern
W : Weight
NeuralNetworks (cont.)
Training Set Function
Tp: Desired output
For calculating error from error function
For calculating optimal or near optimal weights
NeuralNetworks (Multi-Layer Perceptron)
Multi-layer Perceptron
• Set (P) of several perceptrons , each of which classifies the input data differently, can be combined via an additional perceptron which receives all outputs from (P) as input.
NeuralNetworks (Multi-Layer Perceptron)
For calculating error from error function
For calculating optimal or near optimal weights
The output of neuron j in the output layer
NeuralNetworks (Multi-Layer Perceptron)
Multi-layer Perceptron Problems:
• The number of trainable parameters becomes
extremely large.
• It offers little or no invariance to shifting , scaling and other forms of distortion.
• The topology of the input data is completely ignored, yielding similar training results for all permutations of the input data.
These problems are solved by convolution neural networks concepts.
ConvolutionNeuralNetworks
It divided input pattern to set of sub sampling layers .
Each of sub sampling consists of set of features maps that have a slightly smaller resolution than the input pattern.
ConvolutionNeuralNetworks(cont.)
It trained through back propagation because all neurons in one feature map share the same weights.
This lead to an implicit reduction of the gap.
The sub sampling layers have one trainable weight so number of free parameters in it is lower than in conventional layers.
It requires less computational effort than the training of multi-layers perceptrons.
Past, Present and Future
Past• Research about EEG Signal
Present• Study feature extraction and classification
algorithms .
Future• Implement the Classification System.
References
1. David Bouchain “Character Recognition Using ConvolutionalNeural Networks “. Seminar Statistical Learning Theory University of Ulm, Germany Institute for Neural Information Processing ,2006/2007.
2. Christos Stergiou and Dimitrios Siganos” Neural Networks ”.
3. Tian Lan, Deniz Erdogmus, Andre Adami, and Michael Pavel, “Feature Selection by Independent Component Analysis and Mutual
Information Maximization in EEG Signal Classification”, Department of Biomedical Engineering OGI School of Science and Engineering Oregon Health & Science University Beaverton, Oregon 97006, USA.
4. Rave Harpaz ,” Independent Component Analysis: An Introduction”Pattern Recognition Laboratory The Graduate Center, City University of
New York 365 Fifth Avenue New York, NY 10016, USA, Nov/15/ 2005.