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Papers

This material is based upon work supported by the National Science Foundation under Grant Numbers 1065513, 0542947, 0328269, 0208958, 9202100. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Here are links to additional publications in the form of posters, talks and popular press.

2019


  • Advancing the Rehabilitative and Therapeutic Potential of BCI and Noninvasive Sensing Systems.
    Scott, S.M., Raftery, C., and Anderson, C. (2019) In Brain Art Brain-Computer Interfaces for Artistic Expression, ed. by Anton Nijholt, Chapter 12, pp. 327–354, Springer, 2019.
  • Convolutional neural networks for EEG signal classification in asynchronous brain-computer interfaces.
    Elliott M. Forney. (2019) Ph.D.Dissertation, Department of Computer Science, Colorado State University, 2019.
  • A Comparison of Tri-Polar Concentric Ring Electrodes to Disc Electrodes for Decoding Real and Imaginary Finger Movements.
    Saleh Ibrahim Alzahrani. (2019) Ph.D. Dissertation, School of Biomedical Engineering, Colorado State University, 2019.

2018


  • Auditory priming improves neural synchronization in auditory-motor entrainment.
    Crasta, J.E., Thaut, M.H., Anderson, C.W., Davies, P.L., and Gavin, W.J. (2018) Neuropsychologia, vol. 117, pp. 102–112.
  • Comparison of Conventional and Tripolar EEG Electrodes in BCI Paradigms.
    Anderson, C., Besio, W. and Alzahrani, S. (2018) In Proceedings of the Seventh International Brain-Computer Interface Meeting: BCIs, Not Getting Lost in Translation, May, 2018, Asilomar Conference Center, Pacific Grove, California, USA.
  • Mental-Task BCIs Using Convolutional Networks with Label Aggregation and Transfer Learning.
    Forney, E., Anderson, C., Gavin, W. and Davies, P. (2018) In Proceedings of the Seventh International Brain-Computer Interface Meeting: BCIs, Not Getting Lost in Translation, May, 2018, Asilomar Conference Center, Pacific Grove, California, USA.

2016


  • CEBL3: A New Software Platform for EEG Analysis and Rapid Prototyping of BCI Technologies.
    Forney, E., Anderson, C., Gavin, W., Davies, P., Roll, M., Ryzhkov, I., and Vafaei, F. (2016) In Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future, May 30 – June 3 2016, Asilomar Conference Center, Pacific Grove, California, USA, page 145, DOI:10.3217/978-3-85125-467-9-145
  • Detecting P300 ERPs with Convolutional Networks. Forney, E., Anderson, C., Davies, P., Gavin, W., Roll, M. (2016) In Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future, May 30 – June 3 2016, Asilomar Conference Center, Pacific Grove, California, USA, page 206, DOI:10.3217/978-3-85125-467-9-206

2015


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2002


  • 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.

2001


  • Recent Advances in EEG Signal Analysis and Classification
    C. Anderson and D. Peterson (2001) In Clinical Applications of Artificial Neural Networks, ed. by R. Dybowski and V. Gant, Cambridge University Press, UK, chapter 8, pp. 175--191.

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1994