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Table of contents
Abstract
Résumé
Acknowledgements
Table of Contents
List of Figures
List of Tables
Acronyms
1 Introduction
1.1 What are we trying to do?
1.2 Thesis overview
1.3 List of publications
2 Brain-Computer Interfaces: Connecting Brains withMachines
2.1 What is a brain-computer interface (BCI) ?
2.2 How does it work ?
2.3 Examples of EEG-based BCIs
2.4 Constraints
3 Methods of the Neural Interface Engineer
3.1 The nature of EEG signals
3.2 Time-domain analysis
3.3 Frequency-domain analysis
3.4 Filtering EEG signals
3.5 Machine Learning
4 Models and Networks of Attention
4.1 A history of attention modelling
4.2 Integrative model of attention and executive control
4.3 Vocabulary of attention
4.4 Anatomy of attentional networks
4.5 Some neurophysiological effects of attention
5 Modelling of steady-state activity fromtransient potentials
5.1 Introduction
5.2 Transient and steady-state visual evoked potentials
5.3 Materials and methods
5.4 Results
5.5 Discussion
6 Application of SSVEPModelling to Brain-Computer Interfaces
6.1 Introduction
6.2 Materials and methods
6.3 Results
6.4 Validation on a real BCI
6.5 Discussion
7 Prediction of Attentional Load during a Continuous Task
7.1 Introduction
7.2 Materials and methods
7.3 Subjective feedback
7.4 Results
7.5 Discussion
8 Conclusion and Perspectives
A Papers as first author
A.1 Transient brain activity explains the spectral content of steady-state visual evoked potentials
A.2 Detection of steady-state visual evoked potentials using simulated trains of transient evoked potentials.
A.3 Towards cognitive BCI: Neural correlates of sustained attention in a continuous performance task.
A.4 A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback
B Visuals of experiments
B.1 Stimulations for visual evoked potentials
B.2 Continuous performance task
B.3 Serial reading task
C Magnified time-frequency maps
C.1 Visual evoked potential, average on 10 subjects
C.2 Average VEP, subject 4
C.3 Average VEP, subject 6
Bibliography
