Computer Science 250-PBI
Some Initial Readings and Topics

Overviews, Starting Points, General Papers

• K. Kuikkaniemi et al., The Influence of Implicit and Explicit Biofeedback in First-Person Shooter Games, ACM CHI 2010 Conference [Link]
• D. Plass-Oude Bos et al., Human-Computer Interaction for BCI Games, 2010 International Conference on Cyberworlds [Link]
• M. Poel et al., Brain Computer Interfaces as Intelligent Sensors for Enhancing Human-Computer Interaction, ICMI 2012 Conference [Link]
• Fairclough, Physiological Computing: Interfacing with the Human Nervous System, book chapter [Link]
• Nijholt, Towards Multimodal, Multi-party, and Social Brain-Computer Interfacing, 2011 INTETAIN Conference [Link]

Adaptive User Interfaces

• Liu, C., Agrawal, P., Sarkar, N., and Chen, S., Dynamic difficulty adjustment in computer games through real-time anxiety-based affective feedback. Intl. Journal of Human Computer Interaction, 2009 [Link]
• Parasuraman, R., Mouloua, M. and Molloy, R. Effects of adaptive task allocation on monitoring of automated systems. Human Factors, vol. 38, no. 4, pp. 665-679, 1996. [Link]  [Link, can obtain via Tufts library web login]
• T. Lavie, J. Meyer, Benefits and Costs of Adaptive User Interfaces, Int. J. Human-Computer Studies, 68(2010) 508-524. [Link]
• Ludo Maat and Maja Pantic, Gaze-X: Adaptive affective multimodal interface for single-user office scenarios, Proceedings of the 8th International Conference on Multimodal Interfaces (ICMI'06). ACM, New York, NY, USA, pp. 171-178, 2006. [Link, can obtain via Tufts library web login]
• F. Buttussi and L. Chittaro, MOPET: A context-aware and user-adaptive wearable system for fitness training. Artificial Intelligence in Medicine (2008) vol. 42, pp. 153-163. [Link]
• Saulnier, P., Sharlin, E. and Greenberg, S. Using bio-electrical signals to influence the social behaviours of domesticated robots Proceedings of the 4th ACM/IEEE international conference on Human robot interaction, ACM, La Jolla, California, USA, 2009.
• Pomplun, M. and Sunkara, S. Pupil Dilation as an Indicator of Cognitive Workload in Human-Computer Interaction. Human-Centred Computing: Cognitive, Social, and Ergonomic Aspects. Vol. 3 of the Proceedings of the 10th International Conference on Human-Computer Interaction, HCII 2003, Crete, Greece, 542-546. [Link]

Interruptions, other non-invasive, subtle or lightweight user interfaces

• Pomplun, M. and Sunkara, S. Pupil Dilation as an Indicator of Cognitive Workload in Human-Computer Interaction. Human-Centred Computing: Cognitive, Social, and Ergonomic Aspects. Vol. 3 of the Proceedings of the 10th International Conference on Human-Computer Interaction, HCII 2003, Crete, Greece, 542-546. [Link]
• Iqbal, S. T., Zheng, X. S., and Bailey, B. P. 2004. Task-evoked pupillary response to mental workload in human-computer interaction. In CHI '04 Extended Abstracts on Human Factors in Computing Systems (Vienna, Austria, April 24 - 29, 2004). CHI '04. ACM Press, New York, NY, 1477-1480[Link]
• E. Horvitz, A. Jacobs, D. Hovel, "Attention-Sensitive Alerting," Proceedings of UAI '99, Conference on Uncertainty and Artificial Intelligence, July 1999, Morgan Kaufmann: San Francisco. pp. 305-313. [Link]
• Saulnier, P., Sharlin, E. and Greenberg, S. Using bio-electrical signals to influence the social behaviours of domesticated robots Proceedings of the 4th ACM/IEEE international conference on Human robot interaction, ACM, La Jolla, California, USA, 2009.
• Krzysztof Z. Gajos, Katherine Everitt, Desney S. Tan, Mary Czerwinski, Daniel S. Weld, Predictability and Accuracy in Adaptive User Interfaces. ACM CHI 2008 paper (emailed to class)
• D. Chen and R. Vertegaal, Using Mental Load for Managing Interruptions in Physiologically Attentive User Interfaces, ACM CHI Conference 2004 [Link]

Machine Learning and Data Fusion Issues

• R. Fiebrink, D. Trueman, and P.R. Cook, A Meta-Instrument for Interactive, On-the-fly Machine Learning. Proc. NIME'09 Conference on New Interfaces for Musical Expression, 2009. [Link].
• Izzetoglu M, Devaraj A, Bunce S, Onaral B, (2005). Motion Artifact Cancellation in NIR Spectroscopy Using Wiener Filtering. IEEE Transactions on Biomedical Engineering, 52(5):934-938 [Link] [Alternate link]
• Gevins et al. Towards measurement of brain function in operational environments. Biological Psychoogy 1995 May;40(1-2):169-86.
• Noel, J., Baue, K., Lanning, J. Improving pilot mental workload classifcation through feature exploitation and combination: a feasibility study. Computers & Operations Research 32 (2005) 2713-2730.
• Coyle, S., Ward, T., Markham, C. Physiological Noise in Near-infrared Spectroscopy: Implications for Optical Brain Computer Interfacing. Proc. of the 26th Annual International Conference of the IEEE EMBS (2004)
• Haihong, Z., Cuntai, G. A Kernel-based Signal Localization Method for NIRS Brain-computer Interfaces. ICPR (2006)
• Sitaram et al. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. NeuroImage 34 (2007) 1416-1427.

Evaluation issues for adaptive user interfaces

• M. Heymann and A. Degani, Formal Analysis and Automatic Generation of User Interfaces: Approach, Methodology, and an Algorithm. Human Factors, 2007, vol. 49, no. 2, pp. 311-330. [Link]
• S. Weibelzahl, Problems and Pitfalls in Evaluating Adaptive Systems. [Link]
• Mainly the last 3 pages: L. Findlater and K.Z. Gajos, Design Space and Evaluation Challenges of Adaptive Graphical User Interfaces. AI Magazine, Winter 2009, pp. 68-73. [Link]

Direct Input

• G. Cohn, D. Morris, S.N. Patel, D.S. Tan, Your Noise is My Command: Sensing Gestures Using the Body as an Antenna, ACM CHI 2011 Conference [Link]
• T.S. Saponas, D.S. Tan, D. Morris, J. Turner, J.A. Landay, Making Muscle-Computer Interfaces More Practical, ACM CHI Conference 2010 [Link]

Technology

• Webcam pulse detection [Link to code on github]

More on Physiological Measurement:

• Your nominations are welcome

More on Brain Measurement: General

• Millan, J. d., Renkens, F., Mourio, J., and Gerstner, W. 2004. Brain-actuated interaction. Artif. Intell. 159, 1-2 (Nov. 2004), 241-259.
• St. John, M., Kobus, D. A., Morrison, J. G., Schmorrow, D. Overview of the DARPA Augmented Cognition Technical Integration Experiment International Journal of Human-Computer Interaction , 2004, Vol. 17, No.2, Pages 131-149 [Link] [Alternate link]
• Izzetoglu, K., Bunce, S., Onaral, B., Pourrezaei, K. and Chance, B., Functional Optical Brain Imaging Using Near-Infrared During Cognitive Tasks, International Journal of Human-Computer Interaction, 17 (2), pp. 211-231.
• Cichocki, A., Washizawa, Y., Rutkowski, T., Bakardjian, H., Phan, A.-H., Choi, S., Lee, H., Zhao, Q., Zhang, L. and Li, Y. Noninvasive BCIs: Multiway Signal-Processing Array Decompositions. IEEE Computer, Special issue on Brain Computer Interaction, volume 41, no. 10, Oct 2008, pp. 34-42. [Link] (IEEE Xplore digital library, access from tufts.edu or else via Tisch library web page)
• Yoichi Miyawaki et al, Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders, Neuron 60, pp. 915-929, December 11, 2008 Elsevier Inc.

More on Brain Measurement: Emotion/Affect

• Davidson, R.J., What does the prefrontal cortex "do" in affect: Perspectives on frontal EEG asymmetry research, Biological Psychology 67 (2004) 219-233. [Link to journal]
• Davidson R.J., Sutton S.K. Affective neuroscience: the emergence of a discipline. Current Opinion in Neurobiology, Volume 5, Number 2, April 1995 , pp. 217-224(8)
• Jose Leon-Carrion, Juan Francisco Martin-Rodriguez, Jesus Damas-Lopez, Kambiz Pourrezai, Kurtulus Izzetoglu, Juan Manuel Barroso y Martin, Maria Rosario Dominguez-Morales. A lasting post-stimulus activation on dorsolateral prefrontal cortex is produced when processing valence and arousal in visual affective stimuli. Neuroscience Letters 422 2007
• K. Phan, S. Taylor, R. Welsh, L. Decker, D. Noll, T. Nichols, J. Britton, I. Liberzon. Activation of the medial prefrontal cortex and extended amygdala by individual ratings of emotional arousal: a fMRI study. Biological Psychiatry, Volume 53, Issue 3, Pages 211-215
• Joseph E. LeDoux. EMOTION: Clues from the Brain (1995) Ann. Rev. Psych.

More on Brain Measurement: fNIRS

• S. Coyle, T. Ward, C. Markham, G. McDarby. "On the Suitability of Near-Infrared Systems for Next Generation Brain Computer Interfaces". World Congress on Medical Physics and Biomedical Engineering, Sydney, Australia, IFMBE, 2003
• Hoshi, Y. Tamura, M. Near-Infrared Optical Detection of Sequential Brain Activation in the Prefrontal Cortex during Mental Tasks. NEUROIMAGE 5, 292-297 (1997)
• Herrmann, M.J., Ehlis, A. C., Fallgatter, A. J. Frontal activation during a verbal-fluency task as measured by near-infrared spectroscopy. Brain Research Bulletin 61 (2003) 52-56.
• Nagamitsu, S., Nagano, M., Yamashita, Y., Takashima, S.Toyojiro Matsuishi. Prefrontal cerebral blood volume patterns while playing video games--A near-infrared spectroscopy study. Brain & Development 28 (2006) 315-321
• "Spatial and temporal analysis of human motor activity using noninvasive NIR topography". Maki A. et al. Med. Phys. 22 (12), Dec 1995.
• "Prefrontal Hypooxygenation during Language Processing Assessed with Near-Infrared Spectroscopy". Falgatter, A. J., Muller, Th. J., Strik, W. K. Neuropsychobiology 1998; 37: 215-218.
• Shirley M Coyle, Tomas E Ward and Charles M Markham. Brain-computer interface using a simplified functional near-infrared spectroscopy system. 2007 J. Neural Eng. 4 219-226 [Link]

More on Brain Measurement: EEG for BCI

• Ferrez, P. Millan, J. You Are Wrong!--Automatic Detection of Interaction Errors from BrainWaves. in Proceedings of the 19th International Joint Conference on Artificial Intelligence, August 2005.
• Gerwin Schalk, Jonathan R. Wolpaw, Dennis J. McFarland, Gert Pfurtscheller. EEG-based communication: presence of an error potential. 2000. Clinical Neurophysiology 111
• Kiern, Z. A., Aunon, J. I. A New Mode of Communication Between Man and His Surroundings. IEEE Transactions on Biomedical Engineering, Vol. 37, No. 12. 1990
• C.W. Anderson and Z. Sijercic. Classification of EEG signals from four subjects during five mental tasks. Intl. Conf. on Engineering Applications of Neural Networks, 407--414, 1996. [Link]
• Kostov, A. Polak, M. Parallel Man-Machine Training in Development of EEG-Based Cursor Control. IEEE Trans. On Rehabilitation Engineering , Vol. 8, No. 2. 2000.
• Wolpaw JR, McFarland DJ, Neat GW, Forneris CA. An EEG-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol. 1991 Mar; vol. 78, no. 3, pp. 252-9.
• Leeb et al., Walking from thoughts: Not the muscles are crucial, but the brain waves!, 8th Annual International Workshop on Presence, PRESENCE 2005, 21-23 September 2005, London. [Link]

Miscellaneous

• Bianchi et al., Which Physiological Components are More Suitable for Visual ERP Based Brain Computer Interface? A Preliminary MEG/EEG Study [Link]
• Fazli et al., Enhanced performance by a hybrid NIRS EEG brain computer interface [Link]
• Coffey et al., Brain machine interfaces in space: Using spontaneous rather than intentionally generated brain signals [Link]
• Berka et al., EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks [Link]

Notes

You are very welcome to suggest other topics and papers.

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