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Umfang: 1 online resource (484 pages)

ISBN: 9780128214145 , 0128214147

Anmerkung: Front Cover -- Current Research in Neuroadaptive Technology -- Copyright -- Contents -- List of contributors -- Preface -- 1 Designing human-computer interaction with neuroadaptive technology -- 1.1 Introduction -- 1.2 Can neuroadaptive technology improve the quality of human-computer interaction? -- 1.3 Design of goals for neuroadaptive technology -- 1.4 Neuroadaptive autonomy: striking a balance -- 1.5 Summary -- Acknowledgment -- References -- 2 Defining neuroadaptive technology: the trouble with implicit human-computer interaction -- 2.1 Introduction -- 2.2 Background -- 2.2.1 Neuroadaptive technology -- 2.2.2 Human-computer interaction -- 2.3 Defining human-computer interaction and neuroadaptive technology -- 2.3.1 Units and their interface -- 2.3.2 Data and information -- 2.3.3 Implicit and explicit -- 2.3.4 Control and interaction -- 2.3.5 System and user -- 2.3.6 The big picture -- 2.4 Sample scenarios -- 2.5 Discussion -- Acknowledgments -- References -- 3 The multi-stage theory of neurofeedback learning: a framework for understanding mechanisms -- 3.1 Introduction -- 3.1.1 Theories of neurofeedback learning -- 3.2 Multi-stage theory -- 3.2.1 Stage 1: striatal learning -- 3.2.2 Stage 2: thalamic consolidation -- 3.2.3 Stage 3: interoceptive homeostasis -- 3.2.4 Evaluation -- 3.2.4.1 How does the model explain learning success? -- 3.2.4.2 What are the optimal feedback parameters? -- 3.2.4.3 Can the theory suggest new protocols or practices? -- 3.3 Extending the theory's reach -- 3.3.1 Beyond EEG neurofeedback -- 3.3.2 Human-computer symbiosis: collaborative learning -- 3.4 Concluding remarks -- Note -- References -- 4 Towards neuroadaptive modeling: assessing the cognitive states of pilots through passive brain-computer interfacing -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Participants -- 4.2.2 EEG set-up and training data. , 4.2.3 Simulator flight -- 4.2.4 Data analysis -- 4.2.4.1 Preprocessing -- 4.2.4.2 Classification -- 4.2.5 Cognitive model -- 4.3 Results -- 4.3.1 Oddball performance -- 4.3.2 Oddball classification and neurophysiology -- 4.3.3 In-flight classification -- 4.3.4 Model improvement -- 4.4 Discussion -- Acknowledgments -- References -- 5 Tangible physiological devices for positive computing, a retrospective -- 5.1 Introduction (the high-level overview) -- 5.2 Background: physiological objects and spaces -- 5.3 Forewords: our take on tangible physiological devices -- 5.4 Generation 1: spatial augmented reality -- 5.5 Generation 2: electronics -- 5.6 Generation 3: modularity -- 5.7 Discussion -- 5.8 Conclusion -- 5.9 Contributions -- References -- 6 The influence of a neuroadaptive game as a distraction from pain: a fNIRS study -- 6.1 Pain, attention, and dynamic difficulty adjustment -- 6.2 Neuroadaptive gaming -- 6.3 Construction of a neuroadaptive game prototype -- 6.4 The creation of the neuroadaptive game -- 6.5 Evaluation of neuroadaptive technology -- 6.6 Testing neuroadaptive technology: an example -- 6.7 Summary and conclusions -- References -- 7 Ecological measures of cognitive impairments in aeronautics: theory and application -- 7.1 Introduction -- 7.2 Methodological considerations -- 7.2.1 Electrode location -- 7.2.2 Pre-processing -- 7.2.3 Further processing and analyses -- 7.2.4 Overcoming limitations -- 7.3 Experimental data -- 7.3.1 Materials and methods -- 7.3.1.1 Participants -- 7.3.1.2 Experimental task and procedure -- 7.3.1.2.1 Flying scenarios -- 7.3.1.2.2 Oddball task -- 7.3.1.2.3 Procedure -- 7.3.2 Measure and analysis -- 7.3.2.1 Behavioral data -- 7.3.2.2 Electroencephalography -- 7.3.3 Results -- 7.3.3.1 Behavioral data -- 7.3.3.2 EEG measures -- 7.3.3.2.1 Electrode location -- 7.3.3.2.2 ERP analysis -- 7.3.3.2.3 Spectral analysis. , 7.4 Discussion -- 7.5 Conclusion -- Ethical statement -- Acknowledgments -- References -- 8 Transcranial Direct-Current Stimulation (tDCS) attenuates perceived temporal demand during simulated laparoscopic tasks -- 8.1 Introduction -- 8.2 Methods -- 8.2.1 Participants -- 8.2.2 Experimental design -- 8.2.3 Laparoscopic tasks -- 8.2.4 Transcranial direct-current stimulation -- 8.2.5 Primary outcome measure - subjective workload -- 8.2.6 Secondary outcome measures -- 8.2.7 Statistical methods -- 8.2.8 Sample size calculation -- 8.3 Results -- 8.3.1 SURG-TLX -- 8.3.2 Overall task completion -- 8.3.3 Completion time -- 8.3.4 Instrument measures of skill -- 8.3.4.1 Movements -- 8.3.4.2 Path length -- 8.3.4.3 Instrument speed -- 8.3.5 Error score -- 8.3.6 Side effects -- 8.3.7 Placebo assessment -- 8.4 Discussion -- 8.4.1 Reduction in perceived TD -- 8.4.2 Technical performance -- 8.4.3 Technical considerations and limitations -- 8.4.4 Real-world significance -- 8.4.5 Longitudinal learning effects -- 8.5 Conclusions -- Acknowledgments -- Note -- References -- 9 Adaptive virtual reality -- 9.1 Introduction -- 9.2 Adaptive VR -- 9.2.1 Adaptive VR as a closed-loop system -- 9.2.2 Types of Adaptive Interactive Mechanisms (AIM) in room-scale VR -- 9.2.2.1 Aesthetics -- 9.2.2.2 Self representation -- 9.2.2.3 Physics -- 9.2.2.4 Objects and agents -- 9.3 Application of adaptive VR -- 9.4 Open loop monitoring for adaptive VR: an example -- 9.4.1 Risk ratio -- 9.4.2 Predicting individual differences based on AIM -- 9.4.3 Predicting behavioral outcomes based on AIM -- 9.5 Conclusions -- References -- 10 Possibilities and pitfalls for the co-registration of mobile EEG and eye-tracking in the study of economic decision-making in naturalistic settings -- 10.1 Introduction -- 10.2 Value processing and the brain -- 10.2.1 Brain structures within the brain valuation network. , 10.2.2 Measures used to elicit subjective values -- 10.2.3 Electrophysiological correlates -- 10.3 Embodied decision making and the MoBI approach -- 10.3.1 Practical considerations/methodological issues for measuring ERPs in the wild -- the co-registration of EEG and eye-tracking -- 10.3.2 MoBI and eye-tracking solutions for real-world neuroimaging -- 10.4 Using mobile EEG and eye-tracking to examine economic decisions for products -- 10.4.1 Methods -- 10.4.2 Results -- 10.4.3 Discussion and conclusions -- 10.5 General summary and future directions -- Acknowledgments -- References -- 11 The impact of electrode shifts on BCI classifier accuracy -- 11.0 Introduction -- 11.1 Theoretical background -- 11.1.1 Influence of electrode shifts on the recorded data -- 11.1.2 Spatial filter weight of the shifted electrodes -- 11.2 Investigation of electrode shifts in BCI -- 11.2.1 Hypotheses and research questions -- 11.2.2 Methods -- 11.2.2.1 Paradigm -- 11.2.2.2 Data generation -- 11.2.2.3 Classification -- 11.2.2.4 Data analysis -- 11.2.3 Results -- 11.2.4 Discussion -- 11.3 Conclusion -- References -- 12 Walking improves the performance of a brain-computer interface for group decision making -- 12.1 Introduction -- 12.2 Methods -- 12.2.1 Participants -- 12.2.2 Experiment -- 12.2.3 Data recording and pre-processing -- 12.2.4 Neural-based confidence estimation -- 12.2.5 Group decisions -- 12.3 Results -- 12.3.1 Individual behavioral performance while walking and sitting -- 12.3.2 Event-related potential analysis between walking and sitting conditions -- 12.3.3 Group performance -- 12.4 Discussions and conclusions -- Acknowledgments -- References -- 13 Spontaneous radiofrequency emission from electron spins within Drosophila: a novel biological signal -- 13.1 Introduction -- 13.1.1 Chirally induced spin polarization. , 13.1.2 Detection of radiofrequency emissions -- 13.1.3 Methods -- 13.1.4 C-band (4.5-4.8 GHz) waveguide setup -- 13.1.5 S-band (2.6 GHz) resonator setup -- 13.2 Results -- 13.2.1 General remarks -- 13.2.2 Waveguide experiments at 4.5-4.8 GHz -- 13.3 Resonator experiments at 2.6 GHz -- 13.3.1 Slow recordings -- 13.3.2 Fast recordings -- 13.4 Radiofrequency emission during activation of the nervous system by temperature-sensitive cation channels -- 13.4.1 Discussion -- Contributions -- Acknowledgments -- References -- Abstracts from the First Neuroadaptive Technology Conference, 2017 -- COGNITIVE PROBING FOR AUTOMATED NEUROADAPTATION -- ABSTRACT -- INTRODUCTION -- MATERIALS AND METHODS -- RESULTS -- CONCLUSION -- REFERENCES -- ENDOWING THE MACHINE WITH ACTIVE INFERENCE: A GENERIC FRAMEWORK TO IMPLEMENT ADAPTIVE BCI -- ABSTRACT -- INTRODUCTION -- METHODS -- RESULTS -- CONCLUSION -- REFERENCES -- LEARNING FROM LABEL PROPORTIONS IN BCI: A SYMBIOTIC DESIGN FOR STIMULUS PRESENTATION AND SIGNAL DECODING -- ABSTRACT -- INTRODUCTION -- MATERIALS AND METHODS -- RESULTS -- DISCUSSION -- CONCLUSION -- REFERENCES -- FROM UNIVARIATE TO MULTIVARIATE ANALYSIS OF fNIRS DATA -- ABSTRACT -- INTRODUCTION -- MATERIALS AND METHODS -- RESULTS -- DISCUSSION -- REFERENCES -- A GENERALIZED DEEP LEARNING FRAMEWORK FOR CROSS-DOMAIN LEARNING IN BRAIN COMPUTER INTERFACES -- ABSTRACT -- INTRODUCTION -- MATERIALS AND METHODS -- DISCUSSION -- CONCLUSION -- REFERENCES -- TIME-FREQUENCY SENSITIVITY CHARACTERIZATION OF SINGLE-TRIAL OSCILLATORY EEG COMPONENTS -- ABSTRACT -- INTRODUCTION -- MATERIALS AND METHODS -- RESULTS -- DISCUSSION -- CONCLUSION -- REFERENCES -- EEG, ECG AND EOG RESPONSES TO AUTOMATED, REAL DRIVING -- ABSTRACT -- INTRODUCTION -- MATERIALS AND METHODS -- RESULTS -- DISCUSSION -- CLASSIFICATION OF CONCENTRATION LEVELS USING DEEP NEURAL NETWORKS -- ABSTRACT. , INTRODUCTION.

Weitere Ausg.: Print version: Fairclough, Stephen H. Current Research in Neuroadaptive Technology San Diego : Elsevier Science & Technology,c2021 ISBN 9780128214138

Sprache: Englisch