DEVELOPMENT OF A BRAIN-

CONTROLLED FEEDING ROBOT

BY

AIDA KHORSHID TALAB

A dissertation submitted in fulfilment of the

requirement for the degree of Master of

Science in Mechatronics Engineering

Kulliyyah of Engineering

International Islamic University

Malaysia

APRIL 2013

ii

ABSTRACT

Feeding difficulty and malnutrition are common phenomena in disabled people.

Feeding is often time consuming, unpleasant, and may result in asphyxiation. In many

cases, robotic aids are applied to assist disabled people in eating. In particular, for

people with severe disabilities including sensory losses and difficulty in basic physical

mobility, assistive robots that require any movement from users cannot be applicable.

A robotic system which can be controlled merely by thoughts and brain signals would

be quite a remarkable aid for them. Brain Machine Interface (BMI) is a direct

communication pathway between brain and an external electronic device. BMIs aim to

translate brain activities into control commands. To design a system that translates

brain waves to desired commands, motor imagery tasks classification is the core part

of this work. Classification accuracy not only depends on how capable the classifier is

but also on the input data. Feature extraction highlights the properties of signal that

make it distinct from the signals of the other mental tasks. Performance of BMIs

directly depends on the effectiveness of the feature extraction and classification

algorithms. If a feature provides large interclass difference for different classes, the

applied classifier exhibits a better performance. In this work, the application of time

domain features for time-series Electroencephalogram (EEG) signal is discussed.

Time domain features have low computational complexity; thus, they can be

considered as a suitable option for real-time BMI systems. This study includes a

comprehensive assessment of time domain features in which their effectiveness has

been evaluated with two classifiers, namely Support Vector Machine (SVM) and

Fuzzy C-means (FCM). Experimental verifications of the selected combination of

feature and classifier have been done. Based on the requirements for real-time

performance, a prototype of an EEG-based feeding robot has been developed.

Experimental results show that the developed BMI was able to perform the required

tasks in real-time, with tolerable errors of around 17% in average, which can be

further supervised to be reduced or eliminated. For further research, combining some

of the effective features and applying fusion classifiers are suggested to improve the

performance of the BMI system.

iii

خلاصة البحث

صعوبة تناول الطعام و سوء التغذية من الظواهر الشائعة عند المعاقين. في الغالب تكون عملية اطعام هذه الفئة مستهلكة للوقت و غير مستساغة اضافة انها من الممكن ان تسبب الإختناق . في كثير من

لإعانة المعاقين في تناول الطعام. وفي الأخص لمساعدة الفئة التي الحالات يتم تطبيق آليات مساعدة تعاني من إعاقة شديدة تضم الخسارة الحسية و صعوبة إستخدام الربوتات التي تتطلب اي نوع من الحركة

استطاعة رراع آلية يتم التككم هاا بواسطة الأفكار و الاشارات في .من المستخدم في هذه الحالاتالاتصال المباشر بين الدماغ و تعتبر BMI ةالآلي – ن تفيد هذه الحالات.الواجهةالدماغيةالدماغية أ

الجهاز الالكتروني الخارجي . تحاول هذه الواجهة ان تترجم الاشارات الدماغية الى اوامر تحكم. الاساس في عملية تصميم نظام يترجم موجات و انشطة الدماغ الى اوامر تحكم هو التصنيف الصوري لاشارات

ة المصنف فقط بل على نوعية البيانات المدخلة بينما هنا دقة التصنيف لا تعتمد على قدر الدماغ.يعتمد مباشرة BMIأداء الواجهة . استخراج المميزات يركز على خصائص الاشارت الدماغية الاخرى

على فعالية استخراج الخواص و على النظام. أداء الواجهة التصنيف الخوارزمي. ارا كانت الخاصية توفر صفوف فهذا يشير الى فعالية افضل. يناقش هذا العمل نطاق الوقت اختلاف صفي كبير لمختلف التطبيق ميزات نطاق الوقت لإشارات السلسلة الزمنية من اجل EEG يحوي تعقيدا حسابيا منخفضا

-وبالتالي يمكن النظر اليها على انه خيار مناسب لتطبيق نظم الواجهة الدماغية BMIاشارة التخطيط راسة تقييما شاملا لملامح نطاق الوقت الذي تم تقييم فعاليتها مع اثنين من تشمل هذه الد .الآلية

وقد تم تنفيذ عمليات التكقق التجريبي من SVMو معدل FCM المصنفين المسمى: آلية دعم المتجهاتمجموعة مختارة من الميزات والمصنفات. على اسس متطلبات الفعالية في الوقت الحقيقي تم قادر على

BMI . تشير النتائج الأولية الى أن النظامEEG تطوير نمورج أولي لروبوت قائم على المساعدة الذاتيةفي متوسط الحالات 17تحقيق المهام المرادة في الوقت الحقيقي، مع معدل مقبول من الخطأ %.التغذية

، و التي من الممكن مع الإشراف على أن تخفض أو تلغى تماما. يقترح مزيد من البكث في الجمع بين .الآلية-المميزات الفعالة و تطبيق مبدأ انصهار المصنفات لتكسين فعالية نظام الواجهة الدماغية

iv

APPROVAL PAGE

I certify that I have supervised and read this study and that in my opinion; it conforms

to acceptable standards of scholarly presentation and is fully adequate, in scope and

quality, as a thesis for the degree of Master of Science in Mechatronics Engineering.

…………………………………. M. J. E. Salami Supervisor

………………………………….

Md. Raisuddin Khan

Co-Supervisor

I certify that I have read this study and that in my opinion it conforms to acceptable

standards of scholarly presentation and is fully adequate, in scope and quality, as a

thesis for the degree of Master of Science in Mechatronics Engineering.

………………………………….

Rini Akmeliawati

Internal Examiner ………………………………….

Paulraj Murugesa

External Examiner

This thesis was submitted to the Department of Mechatronics Engineering and is

accepted as a fulfilment of the requirement for the degree of Master of Science in

Mechatronics Engineering.

………………………………….

Md. Raisuddin Khan

Head, Department of

Mechatronics Engineering

This thesis was submitted to the Kulliyyah of Engineering and is accepted as a

fulfilment of the requirement for the degree of Master of Science in Mechatronics

Engineering.

………………………………….

Md. Noor Bin Salleh

Dean, Kulliyyah of Engineering

v

DECLARATION

I hereby declare that this dissertation is the result of my own investigations, except

where otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

Aida Khorshid Talab

Signature……………………………. Date ……………………

vi

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND

AFFIRMATION OF FAIR USE OF UNPUBLISHED

RESEARCH

Copyright © 2013 by International Islamic University Malaysia. All rights reserved.

DEVELOPMENT OF A BRAIN-CONTROLLED FEEDING ROBOT

I hereby affirm that The International Islamic University Malaysia (IIUM) holds all

rights in the copyright of this Work and henceforth any reproduction or use in any

form or by means whatsoever is prohibited without the written consent of IIUM. No

parts of this unpublished research may be reproduced, stored in a retrieval system,

or transmitted, in any form or by any means, electronics, mechanical, photocopying,

recording or otherwise without prior written permission of the copying holder.

Affirmed by Aida Khorshid Talab

………………….... ………………….

Signature Date

vii

To Capability and Independency

viii

ACKNOWLEDGEMENTS

Praise to Almighty Allah for His blessings who granted me health and provided me

this opportunity to successfully complete this research work.

I would like to express my profound gratitude and appreciation to my

supervisor, Prof. Dr. M. J. E. Salami, for his supports, encouragement, and guidance

through inventive thoughts and benevolent assistantship all along the study period.

Special thanks to Dr. Md. Raisuddin Khan, my co-supervisor, for his worthwhile

suggestions and generous support at all times.

My deepest appreciations and gratitude goes to my husband, Pouya Foudeh,

my parents, and my parents in-law for their patience, financial and moral support

during my studies. This dissertation would be simply impossible without their support.

My gratitude also goes to Dr. Rini Akmeliawati, for suggesting this topic and

introducing this research area to me.

Last but not least, my sincerest gratitude to all my friends who kindly and

patiently attended as subject in the recording sessions or those who assisted me

whenever I needed their guidance. Finally, to all those who have been there for me, I

thank you for urging me to complete this part of my academic life.

ix

TABLE OF CONTENTS

Abstract ......................................................................................................................... ii

Abstract in Arabic ....................................................................................................... .iii

Approval Page .............................................................................................................. iv

Declaration Page .......................................................................................................... .v

Copyright Page ............................................................................................................. vi

Dedication ................................................................................................................... vii

Acknowledgements .................................................................................................... viii

List of Tables ............................................................................................................. xiii

List of Figures ............................................................................................................ xiv

List of Symbols .......................................................................................................... xix

List of Abbreviations ................................................................................................ xxi

CHAPTER ONE: INTRODUCTION ....................................................................... 1

1.1 Overview...……...…………………………………………………………1

1.2 Problem Statement and its Significance ...................................................... 3

1.3 Research Objectives..................................................................................... 4

1.4 Research Scope ............................................................................................ 4

1.5 Research Methodology ................................................................................ 5

1.6 Organization of the Dissertation .................................................................. 8

CHAPTER TWO: LITERATURE REVIEW. .............................................................. 9

2.1 Introduction……………………………………………………………...... 9

2.2 Brain Organization..................................................................................... 11

2.2.1 Brain Lobes and Their Functions…. ............................................... 11

2.3 EEG Waves Specifications ........................................................................ 13

2.4 EEG Signal Acquisitions ........................................................................... 19

2.5 Recording Conditions ................................................................................ 21

2.6 Electrode Placement .................................................................................. 22

2.7 EEG Signal Preprocessing... ...................................................................... 24

2.7.1 EEG Signal Cleaning and Enhancement ......................................... 25

2.7.2 EEG Signal Segmentation ............................................................... 28

2.8 Feature Extraction. ..................................................................................... 30

2.8.1 Temporal Methods. ......................................................................... 31

2.8.2 Frequential Methods. ...................................................................... 33

2.8.3 Time-Frequency Representations.................................................... 34

2.8.4 Other Feature Extraction Methods. ................................................. 36

2.9 EEG Signal Classification Techniques ...................................................... 36

2.9.1 Artificial Neural Network Classifiers ............................................. 37

x

2.9.2 Hidden Markov Model .................................................................... 38

2.9.3 Bayes quadratic ............................................................................... 38

2.9.4 Linear Discriminant Analysis ......................................................... 39

2.9.5 Support Vector Machine ................................................................. 39

2.9.5.1 Linear Support Vector Machines .................................. 40

2.9.5.2 Nonlinear Support Vector Machines ............................ 46

2.9.6 Fuzzy Classifiers ............................................................................. 47

2.9.7 Hybrid Classifiers ........................................................................... 48

2.9.8 Ensemble Classifiers ....................................................................... 49

2.9.9 Merit and Demerit of Applied Classification Algorithms for EEG. 49

2.10 Existing Brain-Controlled Systems ......................................................... 51

2.11 Existing Assistive feeding Systems ......................................................... 52

2.12 Brain Machine Interface .......................................................................... 54

2.13 Translation to Command ......................................................................... 55

2.14 Controlling an External Device. .............................................................. 56

2.15 Summary .................................................................................................. 56

CHAPTER THREE: EEG SIGNAL ACQUISITION AND PROCESSING…...57

3.1 Introduction ………………………………………………………………57

3.2 EEG Signal Acquisition Equipment ......................................................... 58

3.3 Electrode Placement .................................................................................. 59

3.3.1 EEG Signal Recording Experiment ................................................ 61

3.4 Software and Interface ............................................................................... 62

3.4.1 MATLAB and Simulink. ................................................................ 62

3.4.2 EEGLAB Software.......................................................................... 64

3.5 EEG Signal Acquisition Consideration ..................................................... 66

3.6 EEG Signal Preprocessing ......................................................................... 71

3.6.1 EEG Signal Segmentation ............................................................... 71

3.7 EEG Signal Processing .............................................................................. 71

3.7.1 Feature Extraction. .......................................................................... 72

3.7.1.1 Mean Absolute Value. .......................................................... 74

3.7.1.2 Modified Mean Absolute Value1. ........................................ 75

3.7.1.3 Modified Mean Absolute Value2. ........................................ 76

3.7.1.4 Maximum Value. .................................................................. 77

3.7.1.5 Simple Square Integral. ........................................................ 78

3.7.1.6 Willison Amplitude. ............................................................. 79

3.7.1.7 Waveform Length. ................................................................ 80

3.7.1.8 Integrated EEG. ............................................................. 81

3.7.1.9 Mean Absolute Value Slope. ........................................ 82

3.7.1.10 Root Mean Square....................................................... 83

3.7.1.11 Variance ...................................................................... 84

3.7.1.12 Slope Sign Changes .................................................... 85

xi

3.7.1.13 Standard Deviation...................................................... 86

3.7.1.14 Skewness ..................................................................... 87

3.7.1.15 Kurtosis ....................................................................... 88

3.7.1.16 Mean Value ................................................................. 89

3.7.2 EEG Data Classification ................................................................. 90

3.7.2.1 Support Vector Machine. .............................................. 90

3.7.2.2 Fuzzy C-means. ............................................................ 92

3.7.2.3 FCM Algorithm. ........................................................... 95

3.8 Summary .................................................................................................... 99

CHAPTER FOUR: DESIGN OF THE FEEDING ROBOTIC SYSTEM …....100

4.1 Introduction.............................................................................................. 100

4.2 Proposed System Description .................................................................. 100

4.2.1 Robotic System. ............................................................................ 104

4.3 Forward and Inverse Kinematics for the Gripper-arm. ........................... 109

4.3.1 Inverse Kinematics Solution of two-link Manipulator ................. 110

4.4 Summary .................................................................................................. 118

CHAPTER FIVE: PERFORMANCE EVALUATION...……………….…...…119 5.1 Introduction............................................................................................. .119

5.2 Offline Evaluation. .................................................................................. 120

5.2.1 Mean Absolute Value. ................................................................... 121

5.2.2 Modified Mean Absolute Value1. ................................................. 122

5.2.3 Modified Mean Absolute Value2. ................................................. 123

5.2.4Maximum Value............................................................................. 124

5.2.5 Simple Square Integral. ................................................................. 125

5.2.6 Willison Amplitude. ...................................................................... 126

5.2.7 Waveform Length. ........................................................................ 127

5.2.8 Integrated EEG. ............................................................................. 128

5.2.9 Mean Absolute Value Slope.......................................................... 129

5.2.10 Root Mean Square ....................................................................... 130

5.2.11 Variance ...................................................................................... 131

5.2.12 Slope Sign Changes..................................................................... 132

5.2.13 Standard Deviation ...................................................................... 133

5.2.14 Skewness ..................................................................................... 134

5.2.15 Kurtosis ....................................................................................... 135

5.2.16 Mean Value ................................................................................. 136

5.3 Further Evaluation. .................................................................................. 137

5.4 Further Evaluation and Discussion. ......................................................... 142

5.5 Online Evaluation .................................................................................... 144

5.6 Summary .................................................................................................. 145

xii

CHAPTER SIX: CONCLUSION AND RECOMMENDATION ....................... 146

6.1 Conclusion. .............................................................................................. 146

6.2 Recommendation ................................................................................... 1147

BIBLIOGRAPHY ............................................................................................................ 149

PUBLICATIONS ............................................................................................................. 163

APPENDIX A: SOURCE CODE ............................................................................. 164

xiii

LIST OF TABLES

Table No. Page No.

2.1 Main brain rhythms 16

2.2 Brain electrical potential 18

2.3 Merits and Demerits of Applied Classification Algorithms for EEG 51

4.1 Dynamixel actuator main specifications 109

4.2 Joints angles for desired location for the gripper-arm 114

5.1 Classification accuracy obtained by SVM 138

5.2 Classification accuracy obtained by FCM 139

5.3 Mean and standard deviation obtained by SVM and FCM 141

xiv

LIST OF FIGURES

Figure No. Page No.

1.1 Research methodology chart 7

2.1 Brain structure with four lobes 13

2.2 International 10-20 System of Electrode Placement 23

2.3 International 10-20 System of Electrode Placement with details (a)

side view (b) Up view

24

2.4 Overlap of preprocessing and processing methods 26

2.5 Segmentation (a) Disjoint segmentation (b) Overlapped segmentation 29

2.6 Linear separating hyperplanes for the separable case 42

2.7 Linear separating hyperplanes for the non-separable case 44

2.8 Assistive feeding robots (a) Handy 1 (b) Winsford feeder (c)Neater

Eater (d) My Spoon (e) Meal Buddy (f) Mealtime Partner Dining

System

53

2.9 Brain Computer/Machine Interface 56

3.1 EEG signal or data acquisition, feature extraction and classification 58

3.2 EEG basic recording equipment (a) amplifier, (b) power cable, (c)

USB cable, (d) and applied electrode

60

3.3 Correct way of mounting an EEGcap 61

3.4 Simulink model for signal acquisition 62

3.5 Configuration desirable setting of g.USBamp 63

3.6 Illustration of three channel of EEG signal via Matlab interface 64

3.7 Brain spectrum activity of channel C3, C4, and Cz while imagination

of (a) left hand movements, (b) right hand movements, (c) tongue

movements to the left side of mouth, (d) tongue movements to the

right side of mouth

65

xv

3.8 Brain activity recorded while left hand imagery movement by (a)

channel C3 , (b) channel Cz and (c) channel C4

67

3.9 Brain activity recorded while right hand imagery movement by (a)

channel C3 , (b) channel Cz and (c) channel C4

68

3.10 Brain activity recorded while imagery movement of tongue to the

right side of mouth by (a) channel C3 , (b) channel Cz and (c) channel

C4

69

3.11 Brain activity recorded while imagery movement of tongue to the left

side of mouth by (a) channel C3 , (b) channel Cz and (c) channel C4

70

3.12 Channel Cz, C3, and C4 and two segments of each channel of signal 72

3.13 Distribution of MAV in the feature space 74

3.14 Distribution of MMAV1 in the feature space 75

3.15 Distribution of MMAV2 in the feature space 76

3.16 Distribution of MAX in the feature space 77

3.17 Distribution of SSI in the feature space 78

3.18 Distribution of WAMP in the feature space 79

3.19 Distribution of WL in the feature space 80

3.20 Distribution of IEEG in the feature space 81

3.21 Distribution of MAVS in the feature space 82

3.22 Distribution of RMS in the feature space 83

3.23 Distribution of VAR in the feature space 84

3.24 Distribution of SSC in the feature space 85

3.25 Distribution of STD in the feature space 86

3.26 Distribution of Skewness in the feature space 87

3.27 Distribution of Kurtosis in the feature space 88

3.28 Distribution of MV in the feature space 89

xvi

3.29 Support Vector Machine algorithm chart

91

3.30 Fuzzy C-means algorithm chart 97

4.1 Brain controlled feeding robot 102

4.2 Simulink interface between the recording device and classifier 104

4.3 Block diagram of feeding Robot 105

4.4 Parts of the robot (a) link one (b) link two (c) base (d) gripper 105

4.5 Configuration of the gripper-arm 106

4.6 Parts of the mechanism (a) link one (b) link two (c) base (d) spoon 107

4.7 Configuration of the spoon-arm 107

4.8 Configuration of connection of a series of Dynamixel actuator to a

computer

108

4.9 Dynamixel actuator and invalid angle 109

4.10 Two DOF Planner 112

4.11 Configuration of the gripping arm considering its coverage area 113

4.12 Definition of the tray for the grasping arm 113

4.13 Two possible arrangements of plates in the tray (a) one plate (b) two

plates

115

4.14 Assembled arm-robot and mechanism 116

4.15 proposed system while feeding (a) up view (b) side view 127

5.1 Classification accuracy with FCM and SVM for MAV feature

121

5.2 Classification accuracy with FCM and SVM for MMAV1feature

122

5.3 Classification accuracy with FCM and SVM for MMAV2 feature

123

5.4 Classification accuracy with FCM and SVM for MAX feature 124

5.5 Classification accuracy with FCM and SVM for SSI feature

125

5.6 Classification accuracy with FCM and SVM for WAMP feature

126

xvii

5.7 Classification accuracy with FCM and SVM for WL feature

127

5.8 Classification accuracy with FCM and SVM for IEEG feature

128

5.9 Classification accuracy with FCM and SVM for MAVS feature

129

5.10 Classification accuracy with FCM and SVM for RMS feature

130

5.11 Classification accuracy with FCM and SVM for VAR feature

131

5.12 Classification accuracy with FCM and SVM for SSC feature 132

5.13 Classification accuracy with FCM and SVM for STD feature

133

5.14 Classification accuracy with FCM and SVM for Skewness feature

134

5.15 Classification accuracy with FCM and SVM for Kurtosis feature

135

5.16 Classification accuracy with FCM and SVM for MV feature

136

5.17 Average classification accuracy and standard deviation with SVM 137

5.18 Average classification accuracy and standard deviation with FCM 137

5.19 Average classification accuracy and standard deviation obtained by

SVM and FCM

140

xviii

LIST OF SYMBOLS

E Gaussian white noise

yi Associated label of xi

xi Training point

L Number of observations

α Mapped data point

w Norm to hyperplane

||w|| Euclidean norm of w

d+ The shortest distance from the separating hyperplane to the closest

positive example

d- The shortest distance from the separating hyperplane to the closest

negative example

LP Lagrange multipliers

H Hyperplane

ξi positive slack variables

NS Number of support vectors

µi Lagrange multipliers

C upper bound

Ф Mapping function

si support vectors

i Index of the current point

xix

j Index of the current point

N length of segment

k current segment

wi Transformation matrix

x̄ Mean value of vector x

U fuzzy partition matrix

Mfc fuzzy c-partition space for X

M weighting constant

C number of clusters

cj d-dimension center of the cluster

uij membership value of xi

�̅� vector of the displacements of joints

�̅� vector of End-effector position

li Link length

i Joint angle

xx

LIST OF BREVIATIONS

AAS Average Artifact Subtraction

ANN Artificial Neural Network

AR Auto Regressive

ARBF Adaptive Radial-Basis Function

BCG Ballistocardiograph

BCI Brain Computer Interface

BMI Brain Machine Interface

BP Band Power

CSF CerebroSpinal Fluid

DOF Degree-of-Freedom

DC Direct current

DSP Digital Signal Processing

ECG Electrocardiography

EEG Electroencephalogram

EOG Electrooculography

EP Evoked Potentials

ERD Event-Related Desynchronization

ERP Event-Related Potential

ERS Event-Related Synchronization

xxi

FCM Fuzzy C-means

FIR-MLP Finite Impulse Response Multi-Layer Perceptron

FIS Fuzzy Interface System

fMRI functional Magnetic Resonance Imaging

HMI Human-Machine Interface

HMM Hidden Markov Model

ICA Independent Component Analysis

IEEG Integrated EEG

IKS Inverse Kinematics Solution

IP Instruction Packet

KKT Karush-Kuhn-Tucker

KNN K-Nearest Neighbors

LDA Linear Discriminant Analysis

MAV Mean Absolute Value

MAVS Mean Absolute Value Slope

MAX Maximum Value

MEG Magentoencephalographic

MLP Multi-Layer Perceptron

MMAV Modified Mean Absolute Value

MRP Movement-Related Potentials

MV Mean Value

PC Personal Computer

PCA Principle Component Analysis

PNS Peripheral Nervous System

xxii

PSD Power Spectral Density

RBF-NN Radial Basis Function Neural Networks

RMS Root Mean Square

RP Rapid Prototype

SFCM Supervised Fuzzy C-Means

SOFNN Self-Organizing Fuzzy Neural Network

SP Status Packet

SSC Slope Sign Changes

SSI Simple Square Integral

SSVER Steady-State Visual Evoked Potential

STD Standard Deviation

STFT Short-Time Fourier Transform

SVM Support Vector Machine

TBNN Tree-Based Neural Network

TF Time-Frequency

TTD Thought Translation Device

USB Universal Serial Bus

VAR Variance

WAMP Willison Amplitude

WL Waveform Length

1

CHAPTER ONE

INTRODUCTION

1.1 OVERVIEW

Existing assistive robotic technologies in several cases have taken over the functions

of the caregivers. Assistive robots are one of the solutions by which disabled or

elderly people can be supported to perform their daily activities, such as eating, which

is one of the most essential daily activities.

Various assistive robots have been developed since the late 1980s. Handy1

(Topping and Smith, 1999) is an assistive robot for daily activities. The major

function of Handy1 is to assist the eating process but it can also assist on drinking,

washing, shaving, teeth cleaning, and applying make-up as well. The Winsford feeder

(Hermann, Phalangas, Mahoney, and Alexander, 1999) is a mechanical feeding

system with two choices of input devices for the user, a chin switch or a rocker switch.

Neater Eater is another attempt in the area of feeding robots, which is available

in a manual-operation-type and also in an automatic-operation-type system. The

manual type can be used to suppress the tremors of a user’s upper limbs while a

person eats. My Spoon (Soyama, Ishii and Fukase, 2003) is suitable with Japanese’s

food style. The input device can be selected from one of the following three options:

the chin joystick, reinforcement joystick, and switch.

Meal Buddy (Preston, 2009) scoops the food and then the robotic arm scrapes

the surplus food off the spoon with the rod on the bowls.

The Mealtime Partner Dining System, also known as Mealtime Partners, is

located in front of a user’s mouth. Three bowls can rotate in front of the mouth. The

2

spoon picks up the food and then moves a short distance toward the preset location of

the mouth. In some systems, a beverage straw is located beside the spoon

(Pourmohammadali, Kofman and Khajepour, 2007). There are also some assistive

systems which have been designed for multiple users (Guglielmelli, Lauro, Chiarugi,

Giachetti, Perrella, Pisetta, and Scoglio, 2009). It is evident that the current assistive

feeding robots are inefficient and sometimes unable to assist people with severe

disabilities, including those with sensory losses and/or difficulty in basic physical

mobility. In solving this problem, it is required to come up with a system that uses no

nerves or muscles.

The possibility of brain-computer communication based on EEG signals has

been discussed since almost four decades ago (Vidal, 1973). In 1980s, Apostolos

Georgopoulos found a mathematical relationship between the electrical responses of

single motor-cortex neurons in monkey’s brain and the direction that the monkey

moved his arms. Additionally, It has been demonstrated in several experiments that

brain signals of animals or humans could be interfaced to move mechanisms (Chapin,

Moxon, Markowitz and Nicolelis, 1999; Kennedy, Bakay, Moore, Adams and

Goldwaithe, 2000; Mason and Birch, 2000; Taylor, Tillery and Schwartz, 2002; Black,

Bienenstock, Donoghue, Serruya, Wu and Gao, 2003; Gao, Xu, Cheng, Gao, 2003;

Mussa-Ivaldi and Miller, 2003; Bayliss, 2003; Rebsamen, Burdet, Guan, Zhang, Teo,

Zeng, Ang and Laugier, 2006; Galan, Nuttin, Lew, Ferrez, Vanacker, Philips, Millan

and del, 2008; Ang, Guan, Chua, Ang, Kuah, Wang, Phua, Chin, & Zhang, 2009;

Bin, Gao, Yan, Hong, and Gao, 2009).

Thought Translation Device (TTD) by the Tubingen group (Birbaumer,

Hinterberger, Kubler, and Neumann, 2003), implanted electrodes by Dr. Kennedy’s

group (Kennedy et al., 2000), and Brain Computer Interface (BCI) with telemedicine