Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

PLoS ONE

Volumn 8, Issue 2, 2013, Pages

  Wu, Dongrui ^a   Lance, Brent J ^b   Parsons, Thomas D ^c  

^a GE GLOBAL RESEARCH   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

^c UNIVERSITY OF NORTH TEXAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ACTIVE CLASS SELECTION; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN COMPUTER INTERFACE; COLLABORATIVE FILTERING; ELECTROENCEPHALOGRAM; HUMAN; HUMAN COMPUTER INTERACTION; HUMAN EXPERIMENT; INFORMATION PROCESSING; K NEAREST NEIGHBOR; LEARNING ALGORITHM; LEARNING TRANSFER; MACHINE LEARNING; PROCESS OPTIMIZATION; STIMULUS RESPONSE; STROOP TEST; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; VIRTUAL REALITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN-COMPUTER INTERFACES; COMPUTER SYSTEMS; COOPERATIVE BEHAVIOR; ELECTROENCEPHALOGRAPHY; HUMANS; LEARNING; MALE;

EID: 84874336966     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0056624     Document Type: Article

References (67)

1. Lance BJ, Kerick SE, Ries AJ, Oie KS, McDowell K, (2012) Brain-computer interface technologies in the coming decades. Proc of the IEEE 100: 1585-1599.
2. Fairclough SH, (2009) Fundamentals of physiological computing. Interacting with Computers 21: 133-145.
3. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM, (2002) Brain-computer interfaces for communication and control. Clinical Neurophysiology 113: 767-791.
4. Kerick S, Ries AJ, Oie K, Jung TP, Duann JR, et al. (2011) 2010 neuroscience directors strategic initiative. Technical report, No. ARL-TR-5457, U.S. Army Research Laboratory.
5. Wu D, Courtney CG, Lance BJ, Narayanan SS, Dawson ME, et al. (2010) Optimal arousal identification and classification for affective computing: Virtual Reality Stroop Task. IEEE Trans on Affective Computing 1: 109-118.
6. Parsons TD, Courtney C, Arizmendi B, Dawson M, (2011) Virtual reality Stroop task for neurocog-nitive assessment. Studies in Health Technology and Informatics 143: 433-439.
7. Hoshikawa Y, Yamamoto Y, (1997) Effects of Stroop color-word conflict test on the autonomic nervous system responses. AJP- Heart and Circulatory Physiology 272: 1113-1121.
8. Jin J, Sellers EW, Zhang Y, Daly I, Wang X, et al. (2013) Whether generic model works for rapid erp-based bci calibration. Journal of Neuroscience Methods 212: 94-99.
9. Kareev Y, (1982) Minitypologies from within-subjects designs: Uncovering systematic individual differences in experiments. Journal of Verbal Learning and Verbal Behavior 21: 363-382.
10. Macedonio M, Parsons T, Rizzo A, (2007) Immersiveness and physiological arousal within panoramic video-based virtual reality. Cyberpsychology and Behavior 10: 508-516.
11. Goldberg D, Nichols D, Oki BM, Terry D, (1992) Using collaborative filtering to weave an information tapestry. Communications of the ACM 35: 61-70.
12. Makeig S, Kothe C, Mullen T, Bigdely-Shamlo N, Zhang Z, et al. (2012) Evolving signal processing for braincomputer interfaces. Proc of the IEEE 100: 1567-1584.
13. Zander TO, Kothe C, (2011) Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. Journal of Neural Engineering 8: 025005.
14. Parsons TD, Rizzo AA, (2008) Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: A meta-analysis. Journal of Behavior Therapy and Experimental Psychiatry 39: 250-261.
15. Armstrong C, Reger G, Edwards J, Rizzo A, Courtney C, et al. (2013) Validity of the virtual reality stroop task (VRST) in active duty military. Journal of Clinical and Experimental Neuropsychology 35: 1-11.
16. Burke J, McNeill M, Charles D, Morrow P, Crosbie J, et al. (2009) Optimising engagement for stroke rehabilitation using serious games. The Visual Computer 25: 1085-1099.
17. Flores E, Tobon G, Cavallaro E, Cavallaro FI, Perry JC, et al. (2008) Improving patient motivation in game development for motor deficit rehabilitation. In: Proc. Int'l Conf. on Advances in Computer Entertainment Technology. Yokohama, Japan, 381-384.
18. Parsons T, Rizzo A, Courtney C, Dawson M, (2012) Psychophysiology to assess impact of varying levels of simulation fidelity in a threat environment. Advances in Human-Computer Interaction 5: 1-9.
19. Hunicke R (2005) The case for dynamic difficulty adjustment in games. In: Proc. ACM SIGCHI Int'l Conf. on Advances in computer entertainment technology. Valencia, Spain, 429-433.
20. Juul J (2009) Fear of failing? the many meanings of difficulty in video games. In: Wolf MJP, Perron B, editors, The Video Game Theory Reader 2, NY: Routledge. 237-252.
21. Parsons T, Reinebold J, (2012) Adaptive virtual environments for neuropsychological assessment in serious games. IEEE Transactions on Consumer Electronics 58: 197-204.
22. Yang L, Hanneke S, Carbonell J A theory of transfer learning with applications to active learning. Available: http://www.cs.cmu.edu/~liuy/atl.pdf. Accessed 2013 Jan 15.
23. Rani P, Sarkar N, Liu C (2005) Maintaining optimal challenge in computer games through real-time physiological feedback. In: Proc. 11th Int'l Conf. on Human Computer Interaction. Las Vegas, NV, 184-192.
24. Pan SJ, Yang Q, (2010) A survey on transfer learning. IEEE Trans on Knowledge and Data Engineering 22: 1345-1359.
25. Lomasky R, Brodley CE, Aernecke M, Walt D, Friedl M (2007) Active class selection. In: Proc. 18th European Conference on Machine Learning. Warsaw, Poland, 640-647.
26. MacKay DJC, (1992) Information-based objective functions for active data selection. Neural Computation 4: 589-603.
27. Muslea I, Minton S, Knoblock CA, (2006) Active learning with multiple views. Journal of Artificial Intelligence Research 27: 203-233.
28. Settles B (2009) Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.
29. Zhang Z, Schuller B (2012) Active learning by sparse instance tracking and classifier confidence in acoustic emotion recognition. In: Proc. InterSpeech. Portland, OR.
30. Wu D, Parsons TD (2011) Inductive transfer learning for handling individual differences in affective computing. In: Proc. 4th Int'l Conf. on Affective Computing and Intelligent Interaction. Memphis, TN, volume 2, pp. 142-151.
31. Wu D, Parsons TD (2011) Active class selection for arousal classification. In: Proc. 4th Int'l Conf. on Affective Computing and Intelligent Interaction. Memphis, TN, volume 2, pp. 132-141.
32. Li B, Yang Q, Xue X (2009) Transfer learning for collaborative ltering via a rating-matrix generative model. In: Proc. Int'l Conf. on Machine Learning (ICML). Montreal, Canada, pp. 617-624.
33. Pan W, Liu NN, Xiang EW, Yang Q (2011) Transfer learning to predict missing ratings via heterogeneous user feedbacks. In: Proc. Int'l Joint Conf. on Articial Intelligence (IJCAI). Barcelona, Spain, pp. 2318-2323.
34. Pan W, Xiang EW, Yang Q (2012) Transfer learning in collaborative filtering with uncertain ratings. In: Proc. 26th AAAI Conf. on Artificial Intelligence (AAAI). Toronto, Canada, pp. 662-668.
35. Singh AP, Gordon GJ (2008) Relational learning via collective matrix factorization. In: Proc. ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD). Las Vegas, NV, pp. 650-658.
36. Adams RP, Dahl GE, Murray I (2010) Incorporating side information into probabilistic matrix factorization using Gaussian processes. In: Proc. Conf. on Uncertainty in Articial Intelligence (UAI). Catalina Island, CA, pp. 1-9.
37. Xia Y, Li L, Cao J, Golz M, Mandic DP (2011) A collaborative filtering approach for quasi-brain-death EEG analysis. In: Proc. Int'l Conf. on Acoustics, Speech and Signal Processing. Prague, Czech Republic, pp. 645-648.
38. Li L, Xia Y, Jelfs B, Cao J, Mandic DP, (2012) Modelling of brain consciousness based on collaborative adaptive filters. Neurocomputing 76: 36-43.
39. Wang Y, Jung TP, (2011) A collaborative brain-computer interface for improving human performance. PLoS ONE 6: e20422.
40. Lu S, Guan C, Zhang H, (2009) Unsupervised brain computer interface based on intersubject information and online adaptation. IEEE Trans on Neural Systems and Rehabilitation Engineering 17: 135-145.
41. Vapnik V (1995) The Nature of Statistical Learning Theory. Berlin: Springer-Verlag.
42. Wu P, Dietterich TG (2004) Improving SVM accuracy by training on auxiliary data sources. In: Proc. Int'l Conf. on Machine Learning. Banff, Alberta, Canada, 871-878.
43. Varma S, Simon R (2006) Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7.
44. Wu D, Parsons TD, Mower E, Narayanan SS (2010) Speech emotion estimation in 3D space. In: Proc. IEEE Int'l Conf. on Multimedia & Expo (ICME). Singapore, pp. 737-742.
45. Grimm M, Kroschel K, Narayanan SS (2008) The Vera Am Mittag German audio-visual emotional speech database. In: Proc. Int'l Conf. on Multimedia & Expo (ICME). Hannover, German, pp. 865-868.
46. Lewis D, Catlett J (1994) Heterogeneous uncertainty sampling for supervised learning. In: Proc. Int'l Conf. on Machine Learning (ICML). New Brunswick, NJ, pp. 148-156.
47. Seung H, Opper M, Sompolinsky H (1992) Query by committee. In: Proc. ACM Workshop on Computational Learning Theory. Pittsburgh, PA, 287-294.
48. McCallum A, Nigam K (1998) Employing EM in pool-based active learning for text classification. In: Proc. Int'l Conf. on Machine Learning (ICML). Madison, WI, pp. 359-367.
49. Settles B, Craven M, Ray S (2008) Multiple-instance active learning. In: Advances in Neural Information Processing Systems (NIPS). Vancouver, BC, Canada, pp. 1289-1296.
50. Roy N, McCallum A (2001) Toward optimal active learning through sampling estimation of error reduction. In: Prof. Int'l Conf. on Machine Learning (ICML). Williamstown, MA, pp. 441-448.
51. Cohn D (1994) Neural network exploration using optimal experiment design. In: Proc. Advances in Neural Information Processing Systems (NIPS). Denver, CO, volume 6, pp. 679-686.
52. Xu Z, Akella R, Zhang Y (2007) Incorporating diversity and density in active learning for relevance feedback. In: Proc. European Conference on Information Retrieval (ECIR). Rome, Italy, pp. 246-257.
53. Weiss GM, Provost F, (2003) Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research 19: 315-354.
54. Shi X, Fan W, Ren J (2008) Actively transfer domain knowledge. In: Proc. European Conf. on Machine Learning (ECML). Antwerp, Belgium, 342-357.
55. Parsons T, Courtney C, Rizzo A, Edwards J, Reger G, (2012) Virtual reality paced serial assessment tests for neuropsychological assessment of a military cohort. Studies in Health Technology and Informatics 173: 331-337.
56. Parsons TD, Cosand L, Courtney C, Iyer A, Rizzo AA, (2009) Neurocognitive workload assess-ment using the virtual reality cognitive performance assessment test. Lecture Notes in Artificial Intelligence 5639: 243-252.
57. Parsons TD, Rizzo AA, (2008) Initial validation of a virtual environment for assessment of memory functioning: Virtual reality cognitive performance assessment test. Cyberpsychology and Behavior 11: 17-25.
58. Butler T, Pan H, Tuescher O, Engelien A, Goldstein M, et al. (2007) Human fear-related motor neurocircuitry. Neuroscience 150: 1-7.
59. Stroop J, (1935) Studies of interference in serial verbal reactions. Journal of Experimental Psychology 18: 643-661.
60. Holm S, (1979) A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6: 65-70.
61. Hsu CW, Chang CC, Lin CJ (2010) A practical guide to support vector classification. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 2013 Jan 15.
62. Chang CC, Lin CJ (2009) LIBSVM: A library for support vector machines. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 2013 Jan 15.
63. Xue GR, Dai W, Yang Q, Yu Y (2008) Topic-bridged PLSA for cross-domain text classification. In: Proc. 31st Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR). Singapore, pp. 627-634.
64. Zheng VW, Hu DH, Yang Q (2009) Cross-domain activity recognition. In: Proc. 11th Int'l Conf. on Ubiquitous Computing. Orlando, FL, pp. 61-70.
65. Wu D (2012) Fuzzy sets and systems in building closed-loop affective computing systems for human-computer interaction: Advances and new directions. In: Proc. IEEE World Congress on Computational Intelligence. Brisbane, Australia, pp. 1-8.
66. Gilleade K, Dix A, Allanson J (2005) Affective videogames and modes of affective gaming: Assist me, challenge me, emote me. In: Proc. Digital Games Research Association (DiGRA) Conf. Vancouver, Canada, pp. 16-20.
67. Advance personalized learning. Available: http://www.engineeringchallenges.org/cms/8996/9127.aspx. Accessed 2013 Jan 15.