Update Jan 2021: I'm going to clean existing files, update to python3, and finish the rest of the chapters whenever I have some time. Requirements: Python >= 3.6, numpy >=1.15, scipy >= 1.5, matplotlib >= 3.2, scikit-image >= 0.17

Python (Jupyter notebook) implementations of Analyzing Neural Time Series (2012).

Analyzing Neural Time Series by Mike Cohen is a great book written for neuroscientists working with continuous neural data. Although it may seem like the book is mainly written for EEG analysis, I found that the topics in the book are easily translatable to any domain requiring continuous-data signal processing. Each chapter introduces a new technique, with heavy emphasis on concepts rather than mathematical rigor, and even has summaries at the end of each chapter with tips on how to describe the analysis in the methods section of your paper.

If anything seems off, please let me know.

TODO:

- [x] Chapter 6 cleanup
- [x] Chapter 9 cleanup
- [x] Chapter 10 cleanup
- [x] Chapter 11 cleanup
- [x] Chapter 12 cleanup
- [x] Chapter 13 cleanup
- [x] Chapter 14 cleanup
- [x] Chapter 15 cleanup
- [x] Chapter 16 cleanup
- [x] Chapter 17 cleanup
- [x] Chapter 18 cleanup
- [ ] Chapter 19
- [x] Chapter 20
- [x] Chapter 21 [no figs]
- [ ] Chapter 22 [requires ext ernal methods + topoplot]
- [ ] Chapter 23 [done all exc ept topoplots]
- [x] Chapter 24 [no figs]
- [x] Chapter 25
- [ ] Chapter 26
- [x] Chapter 27
- [ ] Chapter 28 [requires arm orf.m]
- [ ] Chapter 29 [one fig requ ires topoplot]
- [ ] Chapter 30
- [ ] Chapter 31
- [x] Chapter 32 [no figs]
- [x] Chapter 33
- [ ] Chapter 34
- [x] Chapter 35 [no figs]
- [x] Chapter 36 [no figs]

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