Readings will consist of my own lecture notes as well as current research papers on aspects of BCI.

There is no required textbook.

Recommended Reference Text

Pattern Classification. R. Duda, P. Hart, and D. Stork. Wiley-Interscience.
Revised version of a classic. In many ways, what this class addresses is "pattern recognition" where the "patterns" we are looking for are time varying and present in some measurements of neural activity. For those who are worried about the mathematical parts of the course, this is a good reference book.

Extra Reading

For people wanting more background or details, the following books may be useful. There are a few copies of each on reserve in the library.
Spikes: Exploring the Neural Code. F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek. MIT Press.
This is a wonderful book with a good probabilistic treatment of neural coding/decoding.
Neuroscience: Exploring the Brain. M. Bear, B. Connors, and M. Paradiso. Lippencott, Williams and Wilkins Pubs.
This class will not provide much of an introduction to traditional neuroscience. Some familiarity will be assumed but with a good basic reference such as this, everyone should be fine.
An Introduction to Natural Computation. D. Ballard. MIT Press.
While not addressing brain-computer interfaces this book has a mix of neuroscience, math, and computation that is similar to that presented in this course.
Neural Networks for Pattern Recognition. C. Bishop. Oxford.
If you ignore the title, this is a really good book. Most of the mathematical and computational tools we will use are here. They are also very clearly explained. A good reference text.
Probability Theory: The Logic of Science. E. T. Jaynes. (download from the web)
This book provides an introduction to Bayesian probability theory. It is a fun book and it's free.

Concepts and Applications of Inferential Statistics Richard Lowry (download from the web)

A Free, full-length and occasionally interactive statistics textbook. The chapters are also available in PDF.

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