Publications

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Topics

• Our Publications,
• Popular press articles mentioning our work

Our Publications

• Teli, M.N. and Anderson, C.W. (2009) Nonlinear Dimensionality Reduction of Electroencephalogram (EEG) for Brain Computer Interfaces. In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, Sept. 2-6, 2009.
• Anderson, C. W. and Bratman, J.A. (2008) Translating Thoughts Into Actions by Finding Patterns in Brainwave, In Proceedings of the Fourteenth Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, June 2008, pp. 1-6.
• Sokolov, Artem (2007) Analysis of Temporal Structure and Normality in EEG Data, Masters Thesis, Department of Computer Science, Colorado State University, Fort Collins, CO.
• Anderson, C.W., Kirby, M.J., Hundley, D., and Knight, J.N. (2007) Classification of Time-Embedded EEG Using Short-Time Principal Component Analysis, In Towards Brain-Computer Interfacing , edited by G. Dornhege, J. del R. Millan, T. Hinterberger, D.J. McFarland, and K.-R. Muller, The MIT Press, pp. 261-278.
• Teli, Mohammad Nayeem (2007) Dimensionality Reduction and Classification of Time Embedded EEG Signals, Masters Thesis, Department of Computer Science, Colorado State University, Fort Collins, CO.
• Teli, Mohammad Nayeem (2007) Dimensionality Reduction using Neural Networks, ANNIE 2007
• Peterson, D. (2007) Plasticity in EEG Oscillations Associated with Auditory Verbal Learning, Ph.D. Dissertation, Department of Computer Science, Colorado State University.
  Abstract: On a frequent basis, humans need to vocally learn and remember a list of unrelated items. Advances in cognitive neuroscience have begun to identify the brain regions involved. However, the mechanisms by which those regions interact during learning remain elusive. There is growing support for the proposition that the oscillations within and among these regions provide a substrate for their interaction. This proposition is investigated in the present study by evaluating changes in brain oscillations during verbal learning. Previous research in this domain has provided only limited clues about the influence of ecologically significant factors such as repetition and mnemonics on learning performance and brain dynamics. The present study evaluates independent components analysis, power spectral analysis, and coherence of 32-channel electroencephalogram recorded while subjects learned a list of unrelated nouns. The learning task included repetition and either conventional spoken learning or learning with a musical mnemonic. The results show that as subjects make the transition from repetition to learning, their alpha1 frequency band activity undergoes a state transition from synchronized to desynchronized oscillations over right posterior cortex. A similar state transition is observed when learning includes a musical mnemonic, but its topographic distribution in the right hemisphere is reversed and relative desynchrony occurs over right prefrontal cortex. The results suggest that verbal learning, even in the context of repetition, is associated with modulation of brain oscillations and that an anatomically distinct network is recruited when learning includes a musical mnemonic. The study has implications for the basic cognitive neuroscience of learning, clinical rehabilitative applications using learning mnemonics, and the architectures of biologically-plausible machine learning algorithms.
• McFarland, D.J., Anderson, C.W., Muller, K.-R., Schlogl, A., and Krusienski, D.J. (2006) BCI Meeting 2005---Workshop on BCI Signal Processing: Feature Extraction and Translation, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 135--138, June.
• Anderson, C.W., Knight, J.N., O'Connor, T., Kirby, M.J., and Sokolov, A. (2006) Geometric Subspace Methods and Time-Delay Embedding for EEG Artifact Removal and Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 142--146.
• Hundley, D., (2006) Numerical techniques for a Brain to Computer Interface Project. Pacific Northwest Mathematical Association of America (PNW MAA), Ashland Oregon, June 21-24.
• Taxonomy of Feature Extraction and Translation Methods for BCI, unpublished summary for the Third International Meeting on Brain-Computer Interface Technology, June 14-19, 2005.
• Peterson, D., Knight, J., Kirby, M., Anderson, C., and Thaut, M. (2005) Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface, EURASIP Journal on Applied Signal Processing, vol. 19, pp. 3128--3140.
• Anderson, C.W., Knight, J.N., Kirby, M.J. (2005) An Inexpensive Brain-Computer Interface Based on Spatial and Temporal Analysis of EEG. Proceedings of HCI International, (HCI-I) 2005, Las Vegas, NV, (CD-ROM).
• James N. Knight (2003) Signal Fraction Analysis and Artifact Removal in EEG. Masters Thesis, Department of Computer Science, Colorado State University, Fort Collins, CO.
• Kirby, M. and Anderson, C.W. (2003) Geometric Analysis for the Characterization of Nonstationary Time-Series. In Springer Applied Mathematical Sciences Series Celebratory Volume for the Occasion of the 70th Birthday of Larry Sirovich, ed. by Kaplan, E., Marsden, J., and Sreenivasan, K.R., Springer-Verlag, Chapter 8, pp. 263--292.
• Anderson, C.W., and Kirby, M. (2003) EEG Subspace Representations and Feature Selection for Brain-Computer Interfaces. In Proceedings of the 1st IEEE Workshop on Computer Vision and Pattern Recognition for Human Computer Interaction (CVPRHCI), June 17, 2003, Madison, Wisconsin.
• Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H. (2003) Comparison of Linear and Nonlinear Methods for EEG Signal Classification. IEEE Transactions on Neural Systems and Rehabilitative Engineering, vol. 11, no. 2, pp. 141--144. Presented at the second, NIH-sponsored international brain-computer interface workshop titled Brain-Computer Interface Technology: Moving Beyond Demonstrations at the Rensselaerville Institue, New York. (Also see this NIH site.)
• Muller, K.-R., Anderson, C., and Birch, G. (2003) Linear and Non-linear Methods in Brain-Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitative Engineering, vol. 11, no. 2, pp. 162--165, from the panel debating Linear versus Non-linear Methods in BCI Research at the second brain-computer interface workshop titled Brain-Computer Interface Technology: Moving Beyond Demonstrations at the Rensselaerville Institue, New York. (Winner of "The Best TNSRE Paper Award", awarded in 2009 by the editors of the IEEE TNSRE.)
• In 2002, Sarah Matthews, a junior at Poudre High School in Fort Collins, was curious about what differences we might see in EEG when subjects hear tone pairs with varying amounts of dissonance. She designed the experiments, recorded the tones, provided the subjects, and helped record 10-channel EEG signals. Her final report is available here as sarah.doc.
• C. Anderson and D. Peterson (2001) Recent Advances in EEG Signal Analysis and Classification. In Clinical Applications of Artificial Neural Networks, ed. by R. Dybowski and V. Gant, Cambridge University Press, UK, chapter 8, pp. 175--191.
• C. Anderson and Z. Sijercic (1996) Classification of EEG Signals from Four Subjects During Five Mental Tasks. In Solving Engineering Problems with Neural Networks: Proceedings of the Conference on Engineering Applications in Neural Networks (EANN'96), ed. by Bulsari, A.B., Kallio, S., and Tsaptsinos, D., Systems Engineering Association, PL 34, FIN-20111 Turku 11, Finland, pp. 407--414.
• Spatial Analysis of Spontaneous EEG During Cognitive Tasks, 38th meeting of the Society for Psychophysiological Research , Denver, Colorado, September 23-27, 1998.
• C. Anderson, E. Stolz, and S. Shamsunder (1998) Multivariate Autoregressive Models for Classification of Spontaneous Electroencephalogram During Mental Tasks. IEEE Transactions on Biomedical Engineering, vol. 45, no. 3, pp. 277-286.
• C. Anderson (1997) Effects of Variations in Neural Network Topology and Output Averaging on the Discrimination of Mental Tasks from Spontaneous Electroencephalogram. Journal of Intelligent Systems, vol. 11, no. 4, pp. 423-431.
• D. Ford (1996) Analysis of LVQ in the Context of Spontaneous EEG Signal Classification. Masters Dissertation, Department of Computer Science, Colorado State University, Fort Collins, CO 80523.
• S. Devulapalla (1996) Non-Linear Principal Component Analysis and Classification EEG During Mental Tasks, Masters Dissertation, Department of Computer Science, Colorado State University, Fort Collins, CO 80523.
• Z. Sijercic, G.C. Agarwal, and C. Anderson (1996) EEG Signal Compression With ADPCM Subband Coding. In Proceedings of the 39th Midwest Symposium on Circuits and Systems, August, 1996.
• C. Anderson, E. Stolz, and S. Shamsunder (1995) Discriminating Mental Tasks Using EEG Represented by AR Models. Proceedings of the 1995 IEEE Engineering in Medicine and Biology Annual Conference, Sept 20--23, 1995, Montreal, Canada.
• C. Anderson, S. Devulapalli, and E. Stolz (1995) EEG Signal Classification with Different Signal Representations. In Neural Networks for Signal Processing V, ed. by F. Girosi, J. Makhoul, E. Manolakos, E. Wilson, IEEE Service Center, Piscataway, NJ, pp. 475--483.
• C. Anderson, S. Devulapalli, and E. Stolz (1995) Determining Mental State from EEG Signals Using Neural Networks. Scientific Programming, Special Issue on Applications Analysis, 4, 3, 171--183.
• E. Orosz (1994) Classification of EEG Signals Using a Sparse Polynomial Builder, Technical Report 94-111, Computer Science, Colorado State University.

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Publications by others

The following are popular press and newsletter articles in which we are mentioned.

• Patients Put on Thinking Caps, by Kristen Philipkoski, Wired News, January 14, 2005.
• I Think, Therefore I Communicate, By Lakshmi Sandha, in Wired News, July 30, 2003.
• Communicating by Brain Waves, in Psychology Today, May/Jun 2003.
• the Fall, 2002 issue of the Outlook Magazine of the Colorado State University College of Natural Sciences describes our BCI project.

Here is collection of links to other popular press articles and research articles we have previously found to be useful.

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