Previous topic

User evaluation

This Page

Bibliography

[1] AAP Evidence-Based Child and Adolescent Psychosocial Interventions. 2013.
[2] H. Abelson, G. J. Sussman, J. Sussman. Structure and interpretation of computer programs. 1996.
[3] G. D. Abowd, E. D. Mynatt, T. Rodden. The human experience [of ubiquitous computing]. Pervasive Computing, IEEE, 1:48–57, 2002.
[4] L. I. Aftanas, S. A. Golosheikin. Changes in Cortical Activity in Altered States of Consciousness: The Study of Meditation by High-Resolution EEG. Human physiology, 29:143–151, 2003.
[5] E. Angelakis, S. Stathopoulou, J. L. Frymiare, D. L. Green, J. F. Lubar, J. Kounios. EEG Neurofeedback: A Brief Overview and an Example of Peak Alpha Frequency Training for Cognitive Enhancement in the Elderly. The Clinical Neuropsychologist, 21:110–129, 2007.
[6] M. Arns, D. S. Ridder, U. Strehl, M. Breteler, A. Coenen. Efficacy of Neurofeedback Treatment in ADHD: the Effects on Inattention, Impulsivity and Hyperactivity: a Meta-Analysis. Clinical EEG and Neuroscience, 40:180–189, 2009.
[7] D. L. Baggio. Mastering OpenCV with practical computer vision projects: step-by-step tutorials to solve common real-world computer vision problems for desktop or mobile, from augmented reality and number plate recognition to face recognition and 3D head tracking. 2012.
[8] J. Bardram, A. Friday. Ubiquitous Computing Systems. Ubiquitous computing fundamentals, 38–88, 2010.
[9] O. Bazanova. Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis. Journal of Behavioral and Brain Science, 02:239–248, 2012.
[10] M. Beaudouin-Lafon. Designing interaction, not interfaces. Proceedings of the working conference on Advanced visual interfaces, 15–22, 2004.
[11] M. .Y.. Bekkedal, J. Rossi, J. Panksepp. Human brain EEG indices of emotions: Delineating responses to affective vocalizations by measuring frontal theta event-related synchronization. Neuroscience &Biobehavioral Reviews, 35:1959–1970, 2011.
[12] D. Benyon. Designing interactive systems: people, activities, contexts, technologies. 2005.
[13] D. Benyon. Designing interactive systems: a comprehensive guide to HCI and interaction design. 2010.
[14] J. Bloch. Effective Java. 2008.
[15] T. Brandmeyer, A. Delorme. Meditation and Neurofeedback. Frontiers in Psychology, 4:2013.
[16] A. Campbell, T. Choudhury, S. Hu, H. Lu, M. K. Mukerjee, M. Rabbi, R. .D.. Raizada. NeuroPhone: brain-mobile phone interface using a wireless EEG headset. 3, 2010.
[17] D. Coyle, J. Garcia, A. R. Satti, T. M. McGinnity. EEG-based continuous control of a game using a 3 channel motor imagery BCI: BCI game. Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on, 1–7, 2011.
[18] K. Crowley, A. Sliney, I. Pitt, D. Murphy. Evaluating a Brain-Computer Interface to Categorise Human Emotional Response. 2010 10th IEEE International Conference on Advanced Learning Technologies, 276–278, 2010.
[19] A. Delorme, S. Makeig. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134:9–21, 2004.
[20] Distributed systems: concepts and design. 2012.
[21] L. Duan, X. Wang, Z. Yang, H. Zhou, C. Wu, Q. Zhang, J. Miao. An Emotional Face Evoked EEG Signal Recognition Method Based on Optimal EEG Feature and Electrodes Selection. Neural Information Processing, 7062:296–305, 2011.
[22] T. Egner, J. H. Gruzelier. Ecological validity of neurofeedback: modulation of slow wave EEG enhances musical performance. Neuroreport, 14:1221–1224, 2003.
[23] E. T. Esfahani, V. Sundararajan. USING BRAIN–COMPUTER INTERFACES TO DETECT HUMAN SATISFACTION IN HUMAN–ROBOT INTERACTION. International Journal of Humanoid Robotics, 08:87–101, 2011.
[24] S. Finnigan, I. H. Robertson. Resting EEG theta power correlates with cognitive performance in healthy older adults: Resting theta EEG correlates with cognitive aging. Psychophysiology, 48:1083–1087, 2011.
[25] A. Fox, D. Patterson. Engineering software as a service: an agile approach using cloud computing. 2013.
[26] J. Gomez-Gil, I. San-Jose-Gonzalez, L. F. Nicolas-Alonso, S. Alonso-Garcia. Steering a Tractor by Means of an EMG-Based Human-Machine Interface. Sensors, 11:7110–7126, 2011.
[27] D. C. Hammond. What Is Neurofeedback?. Journal of Neurotherapy, 10:25–36, 2007.
[28] P. Hilton, E. Bakker, F. Canedo. Play for Scala: covers Play 2. 2013.
[29] S. I. Hjelm. Research+ design: the making of Brainball. Interactions, 10:26–34, 2003.
[30] S. ichi Ito, Y. Mitsukura, K. Sato, S. Fujisawa, M. Fukumi. Association between ego scores and individual characteristics in EEG analysis: Basic study on individual brain activity.. RO-MAN, 210–215, 2010.
[31] Iso. ISO 9241-210:2010 - Ergonomics of human-system interaction – Part 210: Human-centred design for interactive systems. 2010.
[32] C. Jeremy. Pretotyping@Work. 2012.
[33] R. Khosrowabadi, C. Quek, K. K. Ang, S. W. Tung, M. Heijnen. A Brain-Computer Interface for classifying EEG correlates of chronic mental stress.. IJCNN, 757–762, 2011.
[34] S. R. Klemmer, B. Hartmann, L. Takayama. How bodies matter: five themes for interaction design. Proceedings of the 6th conference on Designing Interactive systems, 140–149, 2006.
[35] J. Kollmann, H. Sharp, A. Blandford. The Importance of Identity and Vision to User Experience Designers on Agile Projects. 11–18, 2009.
[36] N. Kuramoto, S. ichi Ito, K. Sato, S. Fujisawa. Trigger pattern detection method for assisting in ambulation rehabilitation based on EEG analysis.. RO-MAN, 646–652, 2012.
[37] E. A. Larsen, A. I. Wang. Classification of EEG Signals in a Brain- Computer Interface System. M.Sc. Thesis, Norwegian University of Science and Technology - Department of Computer and Information Science, 2011.
[38] J. Lazar. Research methods in human-computer interaction. 2010.
[39] F. Liu. Android native development kit cookbook. 2013.
[40] N. Lofthouse, L. E. Arnold, S. Hersch, E. Hurt, R. DeBeus. A review of neurofeedback treatment for pediatric ADHD.. Journal of attention disorders, 16:351–72, 2012.
[41] W. E. Mackay, A. Fayard. HCI, natural science and design: a framework for triangulation across disciplines. Proceedings of the 2nd conference on Designing interactive systems: processes, practices, methods, and techniques, 223–234, 1997.
[42] I. S. MacKenzie. Human-computer interaction: an empirical research perspective. 2013.
[43] K. Majumdar. Human scalp EEG processing: Various soft computing approaches. Applied Soft Computing, 11:4433–4447, 2011.
[44] M. Marchesi, B. Riccò. BRAVO: a brain virtual operator for education exploiting brain-computer interfaces. 3091, 2013.
[45] R. Matthews, P. J. Turner, N. J. McDonald, K. Ermolaev, T. Manus, R. A. Shelby, M. Steindorf. Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes. Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008:5871–5875, 2008.
[46] D. J. McFarland, J. R. Wolpaw. Brain-computer interfaces for communication and control. Communications of the ACM, 54:60, 2011.
[47] Q. Meng, W. Zhou, Y. Chen, J. Zhou. Feature analysis of epileptic EEG using nonlinear prediction method. Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2010:3998–4001, 2010.
[48] V. Mirgorod. Backbone. js Cookbook. 2013.
[49] A. Nayak. Instant MongoDB. 2013.
[50] NeuroSky. Brain Wave Signal ( EEG ) of NeuroSky, Inc.. 2009.
[51] NeuroSky. ThinkGear Development Guide for Android. 2012.
[52] R. Oostenveld, P. Fries, E. Maris, J.M. Schoffelen. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience, 2011:1–9, 2011.
[53] C. K. Petersen, Klonovs. Development of a Mobile EEG-Based Feature Extraction and Classification System for Biometric Authentication. M.Sc. Thesis, Aalborg University Copenhagen, 2012.
[54] M. Rangaswamy, B. Porjesz, D. B. Chorlian, K. Wang, K. A. Jones, L. O. Bauer, J. Rohrbaugh, S. J. O’Connor, S. Kuperman, T. Reich. Beta power in the EEG of alcoholics. Biological psychiatry, 52:831–842, 2002.
[55] S. Ratabouil. Android NDK discover the native side of Android and inject the power of C/C++ in your applications: beginner's guide. 2012.
[56] G. Rebolledo-Mendez, I. Dunwell, E. A. Martínez-Mirón, M. D. Vargas-Cerdán, S. Freitas, F. Liarokapis, A. R. García-Gaona. Assessing NeuroSky’s Usability to Detect Attention Levels in an Assessment Exercise. Human-Computer Interaction. New Trends, 5610:149–158, 2009.
[57] D. Schmalstieg, A. Bornik, G. Müller-Putz, G. Pfurtscheller. Gaze-directed ubiquitous interaction using a Brain-Computer Interface. 1–5, 2010.
[58] .C.. Stam. Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clinical Neurophysiology, 116:2266–2301, 2005.
[59] A. Stopczynski, C. Stahlhut, M. K. Petersen, J. E. Larsen, C. F. Jensen, M. G. Ivanova, T. S. Andersen, L. K. Hansen. Smartphones as pocketable labs: Visions for mobile brain imaging and neurofeedback. International Journal of Psychophysiology, 2013.
[60] T2. BioZenUsersMan\_1\_7\_0.pdf. 2013.
[61] S. Tong, N. V. Thakor. Quantitative EEG analysis methods and clinical applications. Quantitative EEG analysis methods and clinical applications, 1–107, 2009.
[62] E. R. Tufte. Envisioning Information. 1990.
[63] R. A. Virzi. Refining the test phase of usability evaluation: how many subjects is enough?. Human Factors: The Journal of the Human Factors and Ergonomics Society, 34:457–468, 1992.
[64] J. Wei. Android database programming exploit the power of data-centric and data-driven Android applications with this practical tutorial. 2012.
[65] M. Weiser. The world is not a desktop. interactions, 1:7–8, 1994.
[66] M. Weiser. The Computer for the 21st Century. Scientific American, 265:66–75, 1991.
[67] M. Weiser. Some computer science issues in ubiquitous computing. ACM SIGMOBILE Mobile Computing and Communications Review, 3:12, 1999.
[68] P. Welch. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15:70–73, 1967.
[69] N. E. White, L. Richards. Alpha–theta neurotherapy and the neurobehavioral treatment of addictions, mood disorders and trauma. Introduction to quantitative EEG and neurofeedback advanced theory and applications, 2009.
[70] F. P. Wright. EMOCHAT EMOTIONAL INSTANT MESSAGING WITH THE EPOC HEADSET. M.Sc. Thesis, University of Maryland, Baltimore County, 2010.
[71] Y. Yasui. A Brainwave Signal Measurement and Data Processing Technique for Daily Life Applications. Journal of PHYSIOLOGICAL ANTHROPOLOGY, 28:145–150, 2009.
[72] Y. Yu, D. He, W. Hua, S. Li, Y. Qi, Y. Wang, G. Pan. FlyingBuddy2: A Brain-controlled Assistant for the Handicapped. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 669–670, 2012.
[73] B. Zoefel, R. J. Huster, C. S. Herrmann. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. NeuroImage, 54:1427–1431, 2011.