Signal processing to drive human-computer interaction : EEG and eye-controlled interfaces

Signal processing to drive human-computer interaction : EEG and eye-controlled interfaces /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Nikolopoulos, Spiros (Editor), Kumar, Chandan, (Researcher) (Editor), Kompatsiaris, Yiannis (Editor)
Format: Electronic eBook
Language:English
Published: London, UK : The Institution of Engineering and Technology, 2020.
©2020
Series:IET control, robotics and sensors series ; 129.
Subjects:
Signal processing.
Human-computer interaction.
Biomedical engineering.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Machine generated contents note: 1. Introduction / Ioannis Kompatsiaris
  • 1.1. Background
  • 1.2. Rationale
  • 1.3. Book objectives
  • pt. I Reviewing existing literature on the benefits of BCIs, studying the computer use requirements and modeling the (dis)abilities of people with motor impairment
  • 2. added value of EEG-based BCIs for communication and rehabilitation of people with motor impairment / Ioannis Kompatsiaris
  • 2.1. Introduction
  • 2.2. BCI systems
  • 2.3. Review question
  • 2.4. Methods
  • 2.4.1. Search strategy
  • 2.4.2. Types of participants and model systems
  • 2.4.3. Data synthesis - description of studies-target population characteristics
  • 2.5. EEG-based BCI systems for people with motor impairment
  • 2.5.1. EEG-based BCIs for communication and control
  • 2.5.2. EEG-based BCIs for rehabilitation and training
  • 2.6. Discussion
  • 2.7. Summary
  • References
  • 3. Brain-computer interfaces in a home environment for patients with motor impairment--the MAMEM use case / Ioannis Danglis
  • 3.1. Introduction
  • 3.1.1. Parkinson's disease
  • 3.1.2. Patients with cervical spinal cord injury
  • 3.1.3. Patients with neuromuscular diseases
  • 3.2. Computer habits and difficulties in computer use
  • 3.2.1. Patients with PD
  • 3.2.2. Patients with cervical spinal cord injuries
  • 3.2.3. Patients with NMDs
  • 3.3. MAMEM platform use in home environment
  • 3.3.1. Subjects selection
  • 3.3.2. Method
  • 3.3.3. Results
  • 3.4. Summary
  • References
  • 4. Persuasive design principles and user models for people with motor disabilities / Cees Midden
  • 4.1. Methods for creating user models for the assistive technology
  • 4.1.1. User profiles
  • 4.1.2. Personas
  • 4.2. Persuasive strategies to improve user acceptance and use of an assistive device
  • 4.2.1. Selection of persuasive strategies
  • 4.2.2. Developing persuasive strategies for Phase I: user acceptance and training
  • 4.2.3. Developing persuasive strategies for Phase II: Social inclusion
  • 4.2.4. Conclusions
  • 4.3. Effectiveness of the proposed persuasive and personalization design elements
  • 4.3.1. evaluation of Phase I field trials
  • 4.3.2. evaluation of the assistive technology in a lab study
  • 4.4. Implications for persuasive design requirements
  • 4.4.1. Implication for user profiles and personas
  • 4.4.2. Updated cognitive user profile
  • 4.4.3. Updated requirements for personalization
  • 4.4.4. Updated requirements for persuasive design
  • 4.4.5. Implications for Phase II persuasive design strategies
  • 4.4.6. Conclusions
  • 4.5. Summary
  • References
  • pt. II Algorithms and interfaces for interaction control through eyes and mind
  • 5. Eye tracking for interaction: adapting multimedia interfaces / Steffen Staab
  • 5.1. Tracking of eye movements
  • 5.1.1. Anatomy of the eye
  • 5.1.2. Techniques to track eye movements
  • 5.1.3. Gaze signal processing
  • 5.2. Eye-controlled interaction
  • 5.2.1. Selection methods
  • 5.2.2. Unimodal interaction
  • 5.2.3. Multimodal interaction
  • 5.2.4. Emulation software
  • 5.3. Adapted multimedia interfaces
  • 5.3.1. Adapted single-purpose interfaces
  • 5.3.2. Framework for eye-controlled interaction
  • 5.3.3. Adaptation of interaction with multimedia in the web
  • 5.4. Contextualized integration of gaze signals
  • 5.4.1. Multimedia browsing
  • 5.4.2. Multimedia search
  • 5.4.3. Multimedia editing
  • 5.5. Summary
  • References
  • 6. Eye tracking for interaction: evaluation methods / Steffen Staab
  • 6.1. Background and terminology
  • 6.1.1. Study design
  • 6.1.2. Participants
  • 6.1.3. Experimental variables
  • 6.1.4. Measurements
  • 6.2. Evaluation of atomic interactions
  • 6.2.1. Evaluation of gaze-based pointing and selection
  • 6.2.2. Evaluation of gaze-based text entry
  • 6.3. Evaluation of application interfaces
  • 6.3.1. Comparative evaluation
  • 6.3.2. Feasibility evaluation
  • 6.4. Summary
  • References
  • 7. Machine-learning techniques for EEG data / Ioannis Kompatsiaris
  • 7.1. Introduction
  • 7.1.1. What is the EEG signal?
  • 7.1.2. EEG-based BCI paradigms
  • 7.1.3. What is machine learning?
  • 7.1.4. What do you want to learn in EEG analysis for BCI application?
  • 7.2. Basic tools of supervised learning in EEG analysis
  • 7.2.1. Generalized Rayleigh quotient function
  • 7.2.2. Linear regression modeling
  • 7.2.3. Maximum likelihood (ML) parameter estimation
  • 7.2.4. Bayesian modeling of ML
  • 7.3. Learning of spatial filters
  • 7.3.1. Canonical correlation analysis
  • 7.3.2. Common spatial patterns
  • 7.4. Classification algorithms
  • 7.4.1. Linear discriminant analysis
  • 7.4.2. Least squares classifier
  • 7.4.3. Bayesian LDA
  • 7.4.4. Support vector machines
  • 7.4.5. Kernel-based classifier
  • 7.5. Future directions and other issues
  • 7.5.1. Adaptive learning
  • 7.5.2. Transfer learning and multitask learning
  • 7.5.3. Deep learning
  • 7.6. Summary
  • References
  • 8. BCIs using steady-state visual-evoked potentials / Ioannis Kompatsiaris
  • 8.1. Introduction
  • 8.2. Regression-based SSVEP recognition systems
  • 8.2.1. Multivariate linear regression (MLR) for SSVEP
  • 8.2.2. Sparse Bayesian LDA for SSVEP
  • 8.2.3. Kernel-based BLDA for SSVEP (linear kernel)
  • 8.2.4. Kernels for SSVEP
  • 8.2.5. Multiple kernel approach
  • 8.3. Results
  • 8.4. Summary
  • References
  • 9. BCIs using motor imagery and sensorimotor rhythms / Ioannis Kompatsiaris
  • 9.1. Introduction to sensorimotor rhythm (SMR)
  • 9.2. Common processing practices
  • 9.3. MI BCIs for patients with motor disabilities
  • 9.3.1. MI BCIs for patients with sudden loss of motor functions
  • 9.3.2. MI BCIs for patients with gradual loss of motor functions
  • 9.4. MI BCIs for NMD patients
  • 9.4.1. Condition description
  • 9.4.2. Experimental design
  • 9.5. Toward a self-paced implementation
  • 9.5.1. Related work
  • 9.5.2. SVM-ensemble for self-paced MI decoding
  • 9.5.3. In quest of self-paced MI decoding
  • 9.6. Summary
  • References
  • 10. Graph signal processing analysis of NIRS signals for brain-computer interfaces / Ioannis Kompatsiaris
  • 10.1. Introduction
  • 10.2. NIRS dataset
  • 10.3. Materials and methods
  • 10.3.1. Graph signal processing basics
  • 10.3.2. Dirichlet energy over a graph
  • 10.3.3. Graph construction algorithm
  • 10.3.4. Feature extraction
  • 10.3.5. Classification
  • 10.3.6. Implementation issues
  • 10.4. Results
  • 10.5. Discussion
  • 10.6. Summary
  • References
  • pt. III Multimodal prototype interfaces that can be operated through eyes and mind
  • 11. Error-aware BCIs / Ioannis Kompatsiaris
  • 11.1. Introduction to error-related potentials
  • 11.2. Spatial filtering
  • 11.2.1. Subspace learning
  • 11.2.2. Increasing signal-to-noise ratio
  • 11.3. Measuring the efficiency - ICRT
  • 11.4. error-aware SSVEP-based BCI
  • 11.4.1. Experimental protocol
  • 11.4.2. Dataset
  • 11.4.3. Implementation details - preprocessing
  • 11.4.4. Results
  • 11.5. error-aware gaze-based keyboard
  • 11.5.1. Methodology
  • 11.5.2. Typing task and physiological recordings
  • 11.5.3. Pragmatic typing protocol
  • 11.5.4. Data analysis
  • 11.5.5. System adjustment and evaluation
  • 11.5.6. Results
  • 11.6. Summary
  • References
  • 12. Multimodal BCIs - the hands-free Tetris paradigm / Ioannis Kompatsiaris
  • 12.1. Introduction
  • 12.2. Gameplay design
  • 12.3. Algorithms and associated challenges
  • 12.3.1. Navigating with the eyes
  • 12.3.2. Rotating with the mind
  • 12.3.3. Regulating drop speed with stress
  • 12.4. Experimental design and game setup
  • 12.4.1. Apparatus
  • 12.4.2. Events, sampling and synchronisation
  • 12.4.3. EEG sensors
  • 12.4.4. Calibration
  • 12.5. Data processing and experimental results
  • 12.5.1. Data segmentation
  • 12.5.2. Offline classification
  • 12.5.3. Online classification framework
  • 12.6. Summary
  • References
  • 13. Conclusions / Ioannis Kompatsiaris
  • 13.1. Wrap-up
  • 13.2. Open questions
  • 13.3. Future perspectives.

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