Improving HCI with Brain Input: Review, Trends, and Outlook
暂无分享,去创建一个
[1] Tanja Schultz,et al. Augmented Reality Interface for Smart Home Control using SSVEP-BCI and Eye Gaze , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).
[2] Paul Cairns,et al. Doing Better Statistics in Human-Computer Interaction , 2019 .
[3] Edward Cutrell,et al. BCI for passive input in HCI , 2007 .
[4] Marc Cavazza,et al. Anger-based BCI Using fNIRS Neurofeedback , 2015, UIST.
[5] Marcus A. Magnor,et al. EEG analysis of implicit human visual perception , 2012, CHI.
[6] Matthew Pike,et al. Examining the Reliability of Using fNIRS in Realistic HCI Settings for Spatial and Verbal Tasks , 2015, CHI.
[7] Xuetong Zhai,et al. The NIRS Brain AnalyzIR Toolbox , 2018, Algorithms.
[8] Tanja Schultz,et al. Investigating Intrusiveness of Workload Adaptation , 2014, ICMI.
[9] Franck Tarpin-Bernard,et al. Conceptual Priming for In-game BCI Training , 2015, TCHI.
[10] Johan Eriksson,et al. Effects of interactivity and 3D-motion on mental rotation brain activity in an immersive virtual environment , 2010, CHI.
[11] Robert Oostenveld,et al. The five percent electrode system for high-resolution EEG and ERP measurements , 2001, Clinical Neurophysiology.
[12] Tanja Schultz,et al. Multimodal person independent recognition of workload related biosignal patterns , 2011, ICMI '11.
[13] Hongsong Li,et al. Enhancing Audience Engagement in Performing Arts Through an Adaptive Virtual Environment with a Brain-Computer Interface , 2016, IUI.
[14] Fotis Liarokapis,et al. Examining and Enhancing the Illusory Touch Perception in Virtual Reality Using Non-Invasive Brain Stimulation , 2019, CHI.
[15] Roberto Togneri,et al. Quantifying target spotting performances with complex geoscientific imagery using ERP P300 responses , 2014, Int. J. Hum. Comput. Stud..
[16] Jung-Tai King,et al. An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance , 2016, CHI.
[17] Tanja Schultz,et al. EEG-based Speech Recognition - Impact of Temporal Effects , 2009, BIOSIGNALS.
[18] Klaus-Robert Müller,et al. Introduction to machine learning for brain imaging , 2011, NeuroImage.
[19] Alexander Binder,et al. Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.
[20] Mohan S. Kankanhalli,et al. EEG-based Evaluation of Cognitive Workload Induced by Acoustic Parameters for Data Sonification , 2018, ICMI.
[21] Riccardo Poli,et al. Towards cooperative brain-computer interfaces for space navigation , 2013, IUI '13.
[22] Albrecht Schmidt,et al. Implicit human computer interaction through context , 2000, Personal Technologies.
[23] Arnaud Delorme,et al. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.
[24] Jung-Tai King,et al. To Repeat or Not to Repeat?: Redesigning Repeating Auditory Alarms Based on EEG Analysis , 2019, CHI.
[25] John Paulin Hansen,et al. New technological windows into mind: there is more in eyes and brains for human-computer interaction , 1996, CHI.
[26] Sarah Sharples,et al. Measuring Mental Workload Variations in Office Work Tasks using fNIRS , 2021, Int. J. Hum. Comput. Stud..
[27] Gui-Bin Bian,et al. Removal of Artifacts from EEG Signals: A Review , 2019, Sensors.
[28] Max L. Wilson,et al. Using fNIRS in Usability Testing: Understanding the Effect of Web Form Layout on Mental Workload , 2016, CHI.
[29] D. Boas,et al. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. , 2009, Applied optics.
[30] Nick Bryan-Kinns,et al. Using affective and behavioural sensors to explore aspects of collaborative music making , 2015, Int. J. Hum. Comput. Stud..
[31] Desney S. Tan,et al. Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction , 2010 .
[32] Dennis J. McFarland,et al. Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.
[33] Jung-Tai King,et al. Measuring the Influences of Musical Parameters on Cognitive and Behavioral Responses to Audio Notifications Using EEG and Large-scale Online Studies , 2019, CHI.
[34] Xingda Qu,et al. Affect prediction from physiological measures via visual stimuli , 2011, Int. J. Hum. Comput. Stud..
[35] Daniel Afergan,et al. Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State , 2016, CHI.
[36] Martin Luessi,et al. MNE software for processing MEG and EEG data , 2014, NeuroImage.
[37] Harold W. Thimbleby,et al. User experience evaluation through the brain's electrical activity , 2014, NordiCHI.
[38] Thorsten O. Zander,et al. Towards BCI-Based Implicit Control in Human–Computer Interaction , 2014 .
[39] Alexander Kaplan,et al. Adapting the P300-Based Brain–Computer Interface for Gaming: A Review , 2013, IEEE Transactions on Computational Intelligence and AI in Games.
[40] Harry L. Graber,et al. nirsLAB: A Computing Environment for fNIRS Neuroimaging Data Analysis , 2014 .
[41] Brendan Z. Allison,et al. Is It Significant? Guidelines for Reporting BCI Performance , 2012 .
[42] Heinrich H. Bülthoff,et al. Use the Right Sound for the Right Job: Verbal Commands and Auditory Icons for a Task-Management System Favor Different Information Processes in the Brain , 2018, CHI.
[43] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.
[44] Fabien Lotte,et al. A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces , 2014 .
[45] José Miguel Morales,et al. Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study , 2019, Sensors.
[46] Daniel Afergan,et al. Designing Implicit Interfaces for Physiological Computing , 2015, ACM Trans. Comput. Hum. Interact..
[47] Chi Thanh Vi,et al. Detecting error-related negativity for interaction design , 2012, CHI.
[48] Anton Nijholt,et al. Brain-Computer Interfaces Handbook: Technological and Theoretical Advances , 2018 .
[49] Jérémy Frey,et al. Framework for Electroencephalography-based Evaluation of User Experience , 2016, CHI.
[50] Robert J. K. Jacob,et al. This is your brain on interfaces: enhancing usability testing with functional near-infrared spectroscopy , 2011, CHI.
[51] Desney S. Tan,et al. Using a low-cost electroencephalograph for task classification in HCI research , 2006, UIST.
[52] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[53] Giulio Jacucci,et al. No Need to Laugh Out Loud , 2017, ACM Trans. Comput. Hum. Interact..
[54] Tanja Schultz,et al. Automatic classification of auto-correction errors in predictive text entry based on EEG and context information , 2017, ICMI.
[55] Bilge Mutlu,et al. Pay attention!: designing adaptive agents that monitor and improve user engagement , 2012, CHI.
[56] Daniel Afergan,et al. Dynamic difficulty using brain metrics of workload , 2014, CHI.
[57] Rytis Maskeliunas,et al. Distributed under Creative Commons Cc-by 4.0 Consumer-grade Eeg Devices: Are They Usable for Control Tasks? , 2022 .
[58] Klaus-Robert Müller,et al. Interpretable deep neural networks for single-trial EEG classification , 2016, Journal of Neuroscience Methods.
[59] Gilbert Cockton,et al. Mixing oil and water: transcending method boundaries in assistive technology for traumatic brain injury , 2000, CUU '00.
[60] J. E. Korteling,et al. Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls , 2015, Front. Neurosci..
[61] Thorsten O. Zander,et al. Enhancing Human-Computer Interaction with Input from Active and Passive Brain-Computer Interfaces , 2010, Brain-Computer Interfaces.
[62] Stefan Haufe,et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging , 2014, NeuroImage.
[63] Raja Parasuraman,et al. Neuroergonomics: The Brain at Work , 2006 .
[64] Jeremy Grant,et al. Commercial wireless versus standard stationary EEG systems for personalized emotional brain-computer interfaces: a preliminary reliability check , 2019, Neuroscience Research Notes.
[65] Yijun Wang,et al. Combining Brain-Computer Interface and Eye Tracking for High-Speed Text Entry in Virtual Reality , 2018, IUI.
[66] Tonio Ball,et al. A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping , 2014, IUI.
[67] Chi Thanh Vi,et al. Neuroanatomical Correlates of Perceived Usability , 2017, UIST.
[68] Raja Parasuraman,et al. Wearable functional near infrared spectroscopy (fNIRS) and transcranial direct current stimulation (tDCS): expanding vistas for neurocognitive augmentation , 2015, Front. Syst. Neurosci..
[69] Jie Liu,et al. FOCUS: enhancing children's engagement in reading by using contextual BCI training sessions , 2014, CHI.
[70] Robert J. K. Jacob,et al. Eye Movement-Based Human-Computer Interaction Techniques: Toward Non-Command Interfaces , 2003 .
[71] Satoshi Nakamura,et al. Tracking liking state in brain activity while watching multiple movies , 2017, ICMI.
[72] Anatole Lécuyer,et al. A performance model of selection techniques for p300-based brain-computer interfaces , 2009, CHI.
[73] Joseph G. Makin,et al. Real-time decoding of question-and-answer speech dialogue using human cortical activity , 2019, Nature Communications.
[74] José del R. Millán,et al. Brain-Computer Interfaces , 2020, Handbook of Clinical Neurology.
[75] Paulo Barraza,et al. Implementing EEG hyperscanning setups , 2019, MethodsX.
[76] J. Cacioppo,et al. Inferring psychological significance from physiological signals. , 1990, The American psychologist.
[77] Francisco J. Pelayo,et al. Trends in EEG-BCI for daily-life: Requirements for artifact removal , 2017, Biomed. Signal Process. Control..
[78] Jonathan R Wolpaw,et al. Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis , 2018, Neurology.
[79] E. Musk. An Integrated Brain-Machine Interface Platform With Thousands of Channels , 2019, bioRxiv.
[80] James Tompkin,et al. A novel brain-computer interface using a multi-touch surface , 2010, CHI.
[81] Tanja Schultz,et al. Hybrid fNIRS-EEG based classification of auditory and visual perception processes , 2014, Front. Neurosci..
[82] Roderick Murray-Smith,et al. Simulating the feel of brain-computer interfaces for design, development and social interaction , 2011, CHI.
[83] M. Nitsche,et al. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation , 2000, The Journal of physiology.
[84] Guillaume Gibert,et al. OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.
[85] E. Hildt. Multi-Person Brain-To-Brain Interfaces: Ethical Issues , 2019, Front. Neurosci..
[86] Matthias Scheutz,et al. Brainput: enhancing interactive systems with streaming fnirs brain input , 2012, CHI.
[87] Niels Henze,et al. EngageMeter: A System for Implicit Audience Engagement Sensing Using Electroencephalography , 2017, CHI.
[88] Stefan Haufe,et al. Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.
[89] Jarrod A. Lewis-Peacock,et al. Closed-loop brain training: the science of neurofeedback , 2017, Nature Reviews Neuroscience.
[90] Giulio Jacucci,et al. The Psychophysiology Primer: A Guide to Methods and a Broad Review with a Focus on Human-Computer Interaction , 2016, Found. Trends Hum. Comput. Interact..
[91] Bonnie Brinton Anderson,et al. How Polymorphic Warnings Reduce Habituation in the Brain: Insights from an fMRI Study , 2015, CHI.
[92] Shirley Coyle,et al. On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. , 2004, Physiological measurement.
[93] Chin-Teng Lin,et al. Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials , 2019, CHI.
[94] M.E. Davies,et al. Source separation using single channel ICA , 2007, Signal Process..
[95] Michael X Cohen,et al. Analyzing Neural Time Series Data: Theory and Practice , 2014 .
[96] Athanasios V. Vasilakos,et al. Brain computer interface: control signals review , 2017, Neurocomputing.
[97] Christian Kothe,et al. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.
[98] Robert J. K. Jacob,et al. Combining Electroencephalograph and Functional Near Infrared Spectroscopy to Explore Users' Mental Workload , 2009, HCI.
[99] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[100] Li-Wei Ko,et al. An EEG-based approach for evaluating audio notifications under ambient sounds , 2014, CHI.
[101] Anton Nijholt,et al. BCIforReal: An application-Oriented Approach to BCI Out of the Laboratory , 2017, IUI Companion.
[102] Ara W. Darzi,et al. Imperial College near infrared spectroscopy neuroimaging analysis framework , 2017, Neurophotonics.
[103] Christa Neuper,et al. Using auditory event-related EEG potentials to assess presence in virtual reality , 2012, Int. J. Hum. Comput. Stud..
[104] Roderick Murray-Smith,et al. Hex: Dynamics and Probabilistic Text Entry , 2003, European Summer School on Multi-AgentControl.
[105] Anton Nijholt,et al. Brain–Computer Interface Software: A Review and Discussion , 2020, IEEE Transactions on Human-Machine Systems.
[106] Chris Berka,et al. Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials , 2017, Front. Hum. Neurosci..
[107] Senem Velipasalar,et al. Building predictive models of emotion with functional near-infrared spectroscopy , 2018, Int. J. Hum. Comput. Stud..
[108] Tanja Schultz,et al. Intervention-free selection using EEG and eye tracking , 2016, ICMI.
[109] Fabio Babiloni,et al. The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability , 2019, Sensors.
[110] Robert J. K. Jacob,et al. Using fNIRS brain sensing to evaluate information visualization interfaces , 2013, CHI.
[111] Desney S. Tan,et al. Brain-Computer Interfaces and Human-Computer Interaction , 2010, Brain-Computer Interfaces.
[112] Hasan Ayaz,et al. NEUROERGONOMICS , 2021, Handbook of Human Factors and Ergonomics.
[113] Jérémy Frey,et al. Teegi: tangible EEG interface , 2014, UIST.
[114] Mohammad Soleymani,et al. Short-term emotion assessment in a recall paradigm , 2009, Int. J. Hum. Comput. Stud..
[115] José del R. Millán,et al. Evaluation Criteria for BCI Research , 2007 .
[116] Tanja Schultz,et al. Brain-to-text: decoding spoken phrases from phone representations in the brain , 2015, Front. Neurosci..
[117] Jinrui Zhang,et al. FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data , 2015, BioMed research international.
[118] Klaus-Robert Müller,et al. Open Access Dataset for EEG+NIRS Single-Trial Classification , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[119] Owen Falzon,et al. A comparison of a broad range of EEG acquisition devices – is there any difference for SSVEP BCIs? , 2018, Brain-Computer Interfaces.
[120] Tanja Schultz,et al. Starring into the void?: Classifying Internal vs. External Attention from EEG , 2016, NordiCHI.
[121] A. Pope,et al. Biocybernetic system evaluates indices of operator engagement in automated task , 1995, Biological Psychology.
[122] Robert J. K. Jacob,et al. Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines , 2009, UIST '09.
[123] Desney S. Tan,et al. Feasibility and pragmatics of classifying working memory load with an electroencephalograph , 2008, CHI.
[124] Stephen H. Fairclough,et al. Use of auditory event-related potentials to measure immersion during a computer game , 2015, Int. J. Hum. Comput. Stud..
[125] Wolfram Burgard,et al. Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.
[126] Luca Mainardi,et al. Performance measurement for brain–computer or brain–machine interfaces: a tutorial , 2014, Journal of neural engineering.
[127] Aljo Mujcic,et al. Mental workload vs. stress differentiation using single-channel EEG , 2017 .
[128] A. Barker,et al. NON-INVASIVE MAGNETIC STIMULATION OF HUMAN MOTOR CORTEX , 1985, The Lancet.
[129] Eloisa Vargiu,et al. Brain–Computer Interfaces on Track to Home: Results of the Evaluation at Disabled End-Users’ Homes and Lessons Learnt , 2015, Front. ICT.
[130] Mary L. Cummings,et al. Investigating Mental Workload Changes in a Long Duration Supervisory Control Task , 2015, Interact. Comput..
[131] Robert J. K. Jacob,et al. Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy , 2009, CHI.
[132] Daniel Afergan,et al. Brain-based target expansion , 2014, UIST.
[133] Giulio Jacucci,et al. Physiological Computing , 2015, Computer.
[134] F Babiloni,et al. Passive BCI beyond the lab: current trends and future directions , 2018, Physiological measurement.
[135] Damien Coyle,et al. A Review of Rapid Serial Visual Presentation-based Brain-1 Computer Interfaces 2 , 2017 .
[136] Bertrand Rivet,et al. Adding Human Learning in Brain--Computer Interfaces (BCIs) , 2015, ACM Trans. Comput. Hum. Interact..
[137] Matteo Matteucci,et al. A predictive speller controlled by a brain-computer interface based on motor imagery , 2012, TCHI.
[138] Stephen H. Fairclough,et al. Fundamentals of physiological computing , 2009, Interact. Comput..
[139] Felix Putze,et al. Methods and Tools for Using BCI with Augmented and Virtual Reality , 2019, Brain Art.
[140] Kenneth Kreutz-Delgado,et al. ICLabel: An automated electroencephalographic independent component classifier, dataset, and website , 2019, NeuroImage.
[141] Guido Makransky,et al. Measuring presence in video games: An investigation of the potential use of physiological measures as indicators of presence , 2019, Int. J. Hum. Comput. Stud..
[142] Matthias Scheutz,et al. Sensing cognitive multitasking for a brain-based adaptive user interface , 2011, CHI.
[143] Alissa Nicole Antle,et al. Opening up the Design Space of Neurofeedback Brain--Computer Interfaces for Children , 2018, ACM Trans. Comput. Hum. Interact..
[144] Mihaly Benda,et al. Brain–Computer Interface Spellers: A Review , 2018, Brain sciences.
[145] Michael Goesele,et al. How Human Am I?: EEG-based Evaluation of Virtual Characters , 2017, CHI.
[146] Benjamin Blankertz,et al. Designing for uncertain, asymmetric control: Interaction design for brain-computer interfaces , 2009, Int. J. Hum. Comput. Stud..
[147] Rajesh P. N. Rao,et al. BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains , 2018, ArXiv.
[148] N. Birbaumer,et al. BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.
[149] Sidney K. D'Mello,et al. A Review and Meta-Analysis of Multimodal Affect Detection Systems , 2015, ACM Comput. Surv..
[150] Gilbert Cockton,et al. The "Cyberlink" Brain-Body Interface as an Assistive Technology for Persons with Traumatic Brain Injury: Longitudinal Results from a Group of Case Studies , 1999, Cyberpsychology Behav. Soc. Netw..
[151] James H. Aylor,et al. Computer for the 21st Century , 1999, Computer.
[152] Reinhold Scherer,et al. Mind the Traps! Design Guidelines for Rigorous BCI Experiments , 2018 .
[153] Rajesh P. N. Rao,et al. A Direct Brain-to-Brain Interface in Humans , 2014, PloS one.
[154] Gilbert Cockton,et al. Improving the performance of the cyberlink mental interface with “yes / no program” , 2001, CHI.
[155] Karim Jerbi,et al. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy , 2015, Journal of Neuroscience Methods.
[156] Peta Wyeth,et al. Cooperative Game Play with Avatars and Agents: Differences in Brain Activity and the Experience of Play , 2015, CHI.