Signal Processing to Drive Human-Computer Interaction

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.

• Chapter 1: IntroductionPart I: Reviewing existing literature on the benefits of BCIs, studying the computer use requirements and modeling the (dis)abilities of people with motor impairmentChapter 2: The added value of EEG-based BCIs for communication and rehabilitation of people with motor impairmentChapter 3: Brain-computer interfaces in a home environment for patients with motor impairment - the MAMEM use caseChapter 4: Persuasive design principles and user models for people with motor disabilities Part II: Algorithms and interfaces for interaction control through eyes and mindChapter 5: Eye tracking for interaction: adapting multimedia interfacesChapter 6: Eye tracking for interaction: evaluation methodsChapter 7: Machine-learning techniques for EEG dataChapter 8: BCIs using steady-state visual-evoked potentialsChapter 9: BCIs using motor imagery and sensorimotor rhythmsChapter 10: Graph signal processing analysis of NIRS signals for brain-computer interfaces Part III: Multimodal prototype interfaces that can be operated through eyes and mindChapter 11: Error-aware BCIsChapter 12: Multimodal BCIs - the hands-free Tetris paradigmChapter 13: Conclusions