LIST OF FIGURES
DIAGRAM TITLE
PAGE
2.1 Incremental Learning Model
6 2.2
Mathematical Representation of Incremental Learning Process
7
2.3 Mathematical Incremental Learning Process
with K control 7
3.1 Phases of Project Methodology
20 3.2
Brain region 22
3.3 Occipital Region to Determine VEPsshaded
electrodes 22
3.4 Trial by trial of the visual stimulus
Presentation 23
3.5 The structure of wavelet decomposition
26 4.1
Incremental KNN algorithm 34
4.2 The design of training model for KNN in
WEKA 3.7.12 35
4.3 The design of testing model for KNN in WEKA
3.7.12 35
4.4 Option for KNN classifier model
35 4.5
Algorithm Incremental
Support Vector
Machine 37
4.6 GUI for Incremental SVM apply in Matlab
38 4.7
Algorithm for Hoeffding Tree Induction 40
4.8 Training model for Hoeffding Tree in
Knowledge Flow WEKA 40
4.9 Testing
model for
Hoeffding Tree
in Knowledge Flow WEKA
41
4.10 Option for Hoeffding Tree model
41
5.1 Accuracy and TPR of Incremental K-NN
Model 46
5.2 Accuracy and TPR of Incremental SVM Model
47 5.3
Accuracy and TPR of Hoeffding Tree Model 47
5.4 Accuracy of Incremental Learning Method
49 5.5
TPR of Incremental Learning Method 49
5.6 EEG Signals for Person 5
50 5.7
EEG Signal for subject 10 50
LIST OF ABBREVIATONS
AI - Artificial Intelligence
ANN - Artificial Neural Network
ACC - Accuracy
AUC - Area under ROC curve
TPR - True Positive Rate
BCI - Brain Computer Interfaces
EEG - Electroencephalography
KNN - K-Nearest Neighbour
MLP - Multi-layered Perceptron
PIN - Personal Identification Number
ROC - Receiver Operating Characteristic
SVM - Support Vector Machine
VEP - Visual Evoked Potential
WEKA - Waikato Environment for Knowledge Analysis
IncSVM - Incremental Support Vector Machine
HT - Hoeffding Tree
ANOVA - Analysis of Variance
CHAPTER I
INTRODUCTION
1.1 Project Background
