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Signal Processing to Drive Human-Computer Interaction: EEG and eye-controlled interfaces [Kõva köide]

Edited by (Information Technologies Institute (ITI), Centre for Research & Technology Hellas (CERTH), Greece), Edited by (Institute for Web Science and Technologies, Koblenz, Germany), Edited by (Information Technologies Institute (ITI), Centre for Research & Techn)
  • Formaat: Hardback, 312 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 08-Jul-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785619195
  • ISBN-13: 9781785619199
Teised raamatud teemal:
Signal Processing to Drive Human-Computer Interaction: EEG and eye-controlled interfacesSuurem pilt
  • Formaat: Hardback, 312 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 08-Jul-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785619195
  • ISBN-13: 9781785619199
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781785619199.html
Märksõnad:

In this book, the contributors show how brain-signal processing can be used to drive a human-computer interface (HCI) using processing results from a single modality or a combination. They present concrete usage scenarios and applications in multimedia interfaces, drawing on a wide range of examples covering communications and healthcare.



The evolution of eye tracking and brain-computer interfaces has given a new perspective on the control channels that can be used for interacting with computer applications. In this book leading researchers show how these technologies can be used as control channels with signal processing algorithms and interface adaptations to drive a human-computer interface.

Topics included in the book include a comprehensive overview of eye-mind interaction incorporating algorithm and interface developments; modeling the (dis)abilities of people with motor impairment and their computer use requirements and expectations from assistive interfaces; and signal processing aspects including acquisition, pre-processing, enhancement, feature extraction, and classification of eye gaze, EEG (Steady-state visual evoked potentials, motor imagery and error-related potentials) and near-infrared spectroscopy (NIRS) signals. Finally, the book presents a comprehensive set of guidelines, with examples, for conducting evaluations to assess usability, performance, and feasibility of multi-model interfaces combining eye gaze and EEG based interaction algorithms.

The contributors to this book are researchers, engineers, clinical experts, and industry practitioners who have collaborated on these topics, providing an interdisciplinary perspective on the underlying challenges of eye and mind interaction and outlining future directions in the field.

About the editors xiii
Preface xv
1 Introduction
1(6)
Spiros Nikolopoulos
Chandan Kumar
Ioannis Kompatsiaris
1.1 Background
1(1)
1.2 Rationale
2(2)
1.3 Book objectives
4(3)
Part I Reviewing existing literature on the benefits of BCIs, studying the computer use requirements and modeling the (dis)abilities of people with motor impairment
7(74)
2 The added value of EEG-based BCIs for communication and rehabilitation of people with motor impairment
9(24)
Ioulietta Lazarou
Spiros Nikolopoulos
Ioannis Kompatsiaris
2.1 Introduction
10(1)
2.2 BCI systems
11(2)
2.3 Review question
13(1)
2.4 Methods
14(2)
2.4.1 Search strategy
14(1)
2.4.2 Types of participants and model systems
14(1)
2.4.3 Data synthesis - description of studies-target population characteristics
15(1)
2.5 EEG-based BCI systems for people with motor impairment
16(8)
2.5.1 EEG-based BCIs for communication and control
16(6)
2.5.2 EEG-based BCIs for rehabilitation and training
22(2)
2.6 Discussion
24(1)
2.7 Summary
25(1)
References
26(7)
3 Brain-computer interfaces in a home environment for patients with motor impairment--the MAMEM use case
33(16)
Sevasti Bostantjopoulou
Zoe Katsarou
Ioannis Danglis
3.1 Introduction
33(4)
3.1.1 Parkinson's disease
34(1)
3.1.2 Patients with cervical spinal cord injury
35(1)
3.1.3 Patients with neuromuscular diseases
36(1)
3.2 Computer habits and difficulties in computer use
37(1)
3.2.1 Patients with PD
37(1)
3.2.2 Patients with cervical spinal cord injuries
37(1)
3.2.3 Patients with NMDs
37(1)
3.3 MAMEM platform use in home environment
38(6)
3.3.1 Subjects selection
38(1)
3.3.2 Method
39(2)
3.3.3 Results
41(3)
3.4 Summary
44(1)
References
45(4)
4 Persuasive design principles and user models for people with motor disabilities
49(32)
Sofia Fountoukidou
Jaap Ham
Uwe Matzat
Cees Midden
4.1 Methods for creating user models for the assistive technology
49(4)
4.1.1 User profiles
50(1)
4.1.2 Personas
50(3)
4.2 Persuasive strategies to improve user acceptance and use of an assistive device
53(12)
4.2.1 Selection of persuasive strategies
53(1)
4.2.2 Developing persuasive strategies for Phase I: user acceptance and training
53(6)
4.2.3 Developing persuasive strategies for Phase II: Social inclusion
59(6)
4.2.4 Conclusions
65(1)
4.3 Effectiveness of the proposed persuasive and personalization design elements
65(5)
4.3.1 The evaluation of Phase I field trials
66(1)
4.3.2 The evaluation of the assistive technology in a lab study
67(3)
4.4 Implications for persuasive design requirements
70(7)
4.4.1 Implication for user profiles and personas
70(1)
4.4.2 Updated cognitive user profile
71(2)
4.4.3 Updated requirements for personalization
73(1)
4.4.4 Updated requirements for persuasive design
73(2)
4.4.5 Implications for Phase II persuasive design strategies
75(1)
4.4.6 Conclusions
76(1)
4.5 Summary
77(1)
References
77(4)
Part II Algorithms and interfaces for interaction control through eyes and mind
81(148)
5 Eye tracking for interaction: adapting multimedia interfaces
83(34)
Raphael Menges
Chandan Kumar
Steffen Staab
5.1 Tracking of eye movements
83(6)
5.1.1 Anatomy of the eye
83(2)
5.1.2 Techniques to track eye movements
85(1)
5.1.3 Gaze signal processing
86(3)
5.2 Eye-controlled interaction
89(5)
5.2.1 Selection methods
90(1)
5.2.2 Unimodal interaction
91(1)
5.2.3 Multimodal interaction
92(1)
5.2.4 Emulation software
93(1)
5.3 Adapted multimedia interfaces
94(15)
5.3.1 Adapted single-purpose interfaces
95(7)
5.3.2 Framework for eye-controlled interaction
102(2)
5.3.3 Adaptation of interaction with multimedia in the web
104(5)
5.4 Contextualized integration of gaze signals
109(1)
5.4.1 Multimedia browsing
109(1)
5.4.2 Multimedia search
110(1)
5.4.3 Multimedia editing
110(1)
5.5 Summary
110(1)
References
111(6)
6 Eye tracking for interaction: evaluation methods
117(28)
Chandan Kumar
Raphael Menges
Korok Sengupta
Steffen Staab
6.1 Background and terminology
117(7)
6.1.1 Study design
118(1)
6.1.2 Participants
119(1)
6.1.3 Experimental variables
120(2)
6.1.4 Measurements
122(2)
6.2 Evaluation of atomic interactions
124(5)
6.2.1 Evaluation of gaze-based pointing and selection
124(2)
6.2.2 Evaluation of gaze-based text entry
126(3)
6.3 Evaluation of application interfaces
129(10)
6.3.1 Comparative evaluation
130(5)
6.3.2 Feasibility evaluation
135(4)
6.4 Summary
139(1)
References
140(5)
7 Machine-learning techniques for EEG data
145(24)
Vangelis P. Oikonomou
Spiros Nikolopoulos
Ioannis Kompatsiaris
7.1 Introduction
145(5)
7.1.1 What is the EEG signal?
145(1)
7.1.2 EEG-based BCI paradigms
146(2)
7.1.3 What is machine learning?
148(1)
7.1.4 What do you want to learn in EEG analysis for BCI application?
149(1)
7.2 Basic tools of supervised learning in EEG analysis
150(4)
7.2.1 Generalized Rayleigh quotient function
150(1)
7.2.2 Linear regression modeling
151(1)
7.2.3 Maximum likelihood (ML) parameter estimation
152(1)
7.2.4 Bayesian modeling of ML
153(1)
7.3 Learning of spatial filters
154(2)
7.3.1 Canonical correlation analysis
154(1)
7.3.2 Common spatial patterns
155(1)
7.4 Classification algorithms
156(6)
7.4.1 Linear discriminant analysis
157(1)
7.4.2 Least squares classifier
157(2)
7.4.3 Bayesian LDA
159(1)
7.4.4 Support vector machines
160(1)
7.4.5 Kernel-based classifier
161(1)
7.5 Future directions and other issues
162(1)
7.5.1 Adaptive learning
162(1)
7.5.2 Transfer learning and multitask learning
162(1)
7.5.3 Deep learning
163(1)
7.6 Summary
163(1)
References
163(6)
8 BCIs using steady-state visual-evoked potentials
169(16)
Vangelis P. Oikonomou
Elisavet Chatzilari
Georgios Liaros
Spiros Nikolopoulos
Ioannis Kompatsiaris
8.1 Introduction
169(2)
8.2 Regression-based SSVEP recognition systems
171(7)
8.2.1 Multivariate linear regression (MLR) for SSVEP
172(1)
8.2.2 Sparse Bayesian LDA for SSVEP
173(2)
8.2.3 Kernel-based BLDA for SSVEP (linear kernel)
175(1)
8.2.4 Kernels for SSVEP
175(1)
8.2.5 Multiple kernel approach
176(2)
8.3 Results
178(3)
8.4 Summary
181(1)
References
181(4)
9 BCIs using motor imagery and sensorimotor rhythms
185(26)
Kostas Georgiadis
Nikos A. Laskaris
Spiros Nikolopoulos
Ioannis Kompatsiaris
9.1 Introduction to sensorimotor rhythm (SMR)
185(1)
9.2 Common processing practices
186(1)
9.3 MI BCIs for patients with motor disabilities
187(1)
9.3.1 MI BCIs for patients with sudden loss of motor functions
187(1)
9.3.2 MI BCIs for patients with gradual loss of motor functions
187(1)
9.4 MI BCIs for NMD patients
188(12)
9.4.1 Condition description
188(1)
9.4.2 Experimental design
188(12)
9.5 Toward a self-paced implementation
200(6)
9.5.1 Related work
200(1)
9.5.2 An SVM-ensemble for self-paced MI decoding
200(2)
9.5.3 In quest of self-paced MI decoding
202(4)
9.6 Summary
206(1)
References
206(5)
10 Graph signal processing analysis of NIRS signals for brain-computer interfaces
211(18)
Panagiotis C. Petrantonakis
Ioannis Kompatsiaris
10.1 Introduction
211(2)
10.2 NIRS dataset
213(1)
10.3 Materials and methods
214(4)
10.3.1 Graph signal processing basics
214(1)
10.3.2 Dirichlet energy over a graph
215(1)
10.3.3 Graph construction algorithm
215(1)
10.3.4 Feature extraction
216(1)
10.3.5 Classification
217(1)
10.3.6 Implementation issues
217(1)
10.4 Results
218(5)
10.5 Discussion
223(1)
10.6 Summary
223(1)
References
224(5)
Part III Multimodal prototype interfaces that can be operated through eyes and mind
229(52)
11 Error-aware BCIs
231(30)
Fotis P. Kalaganis
Elisavet Chatzilari
Nikos A. Laskaris
Spiros Nikolopoulos
Ioannis Kompatsiaris
11.1 Introduction to error-related potentials
231(1)
11.2 Spatial filtering
232(6)
11.2.1 Subspace learning
233(2)
11.2.2 Increasing signal-to-noise ratio
235(3)
11.3 Measuring the efficiency - ICRT
238(1)
11.4 An error-aware SSVEP-based BCI
239(6)
11.4.1 Experimental protocol
239(1)
11.4.2 Dataset
240(1)
11.4.3 Implementation details - preprocessing
241(1)
11.4.4 Results
242(3)
11.5 An error-aware gaze-based keyboard
245(11)
11.5.1 Methodology
245(1)
11.5.2 Typing task and physiological recordings
246(1)
11.5.3 Pragmatic typing protocol
247(1)
11.5.4 Data analysis
247(1)
11.5.5 System adjustment and evaluation
248(1)
11.5.6 Results
248(8)
11.6 Summary
256(1)
References
257(4)
12 Multimodal BCIs - the hands-free Tetris paradigm
261(16)
Elisavet Chatzilari
Georgios Liaros
Spiros Nikolopoulos
Ioannis Kompatsiaris
12.1 Introduction
261(1)
12.2 Gameplay design
262(2)
12.3 Algorithms and associated challenges
264(6)
12.3.1 Navigating with the eyes
264(1)
12.3.2 Rotating with the mind
265(3)
12.3.3 Regulating drop speed with stress
268(2)
12.4 Experimental design and game setup
270(2)
12.4.1 Apparatus
270(1)
12.4.2 Events, sampling and synchronisation
271(1)
12.4.3 EEG sensors
271(1)
12.4.4 Calibration
271(1)
12.5 Data processing and experimental results
272(3)
12.5.1 Data segmentation
272(1)
12.5.2 Offline classification
272(3)
12.5.3 Online classification framework
275(1)
12.6 Summary
275(1)
References
276(1)
13 Conclusions
277(4)
Chandan Kumar
Spiros Nikolopoulos
Ioannis Kompatsiaris
13.1 Wrap-up
277(1)
13.2 Open questions
278(1)
13.3 Future perspectives
279(2)
Index 281
Spiros Nikolopoulos is a senior researcher at the Centre for Research and Technology Hellas (CERTH) in the Information Technologies Institute (ITI) Greece. Brain-computer interfaces based on EEG analysis is classified among his main research interests, leading the brain-related activities of his lab. He is the co-author of 22 papers in refereed journals, 9 book chapters and more than 55 conference papers in international conferences.



Chandan Kumar is a post-doctoral researcher, leading the working group of Interactive Web and Human Computing at the Institute for Web Science and Technologies, Koblenz, Germany. His research interests combine the interdisciplinary fields of eye tracking, Web, geovisualization, and human-computer interaction. His research work has been recognized at several international venues with more than 40 publications at peer-reviewed conferences, journals and workshops.



Ioannis Kompatsiaris is a senior researcher at the Centre for Research and Technology Hellas (CERTH) in the Information Technologies Institute (ITI) Greece, where he leads the Multimedia, Knowledge and Social Media Analytics Lab. He is the co-author of 129 papers in refereed journals, 46 book chapters, 8 patents and more than 420 papers in international conferences. He is a senior member of IEEE and member of ACM.