# Wavelet Features Extraction **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [August 3, 2018, 9:48pm UTC](https://mne.discourse.group/t/wavelet-features-extraction/1586 "2018-08-03T21:48:59Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [August 3, 2018, 9:48pm UTC](https://mne.discourse.group/t/wavelet-features-extraction/1586/1 "2018-08-03T21:48:59Z")
External Email - Use Caution Greetings, I use short time Fourier transform to extract features from the time-series EEG signals. Now I plan to use Wavelet that I don't have any prior experience in. Can someone provide a Python implementation (or MNE - btw, I couldn't digest mne morlet for some reason!)? Also, what output I should expect, and what features can be computed from the Wavelet transform output to be ready to fit a learner? A question I would also like to ask those who're concerned about feature extraction, why would I use Wavelet over STFT, or vice versa? Thank you, Badar Almarri Graduate Student Dept. of Computer Science and Engr. University of Connecticut badar.almarri at uconn.edu \<[http://www.linkedin.com/pub/badar-almarri/48/b09/8b8](http://www.linkedin.com/pub/badar-almarri/48/b09/8b8)\> -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20180803/4f3d06ab/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20180803/4f3d06ab/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [August 3, 2018, 11:53pm UTC](https://mne.discourse.group/t/wavelet-features-extraction/1586/2 "2018-08-03T23:53:01Z")
External Email - Use Caution Hi, If you're interested in feature extraction, you might want to look into mne-features: [https://mne-tools.github.io/mne-features/api.html](https://mne-tools.github.io/mne-features/api.html). It should fit your needs. Best, Mainak ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [August 6, 2018, 10:05pm UTC](https://mne.discourse.group/t/wavelet-features-extraction/1586/3 "2018-08-06T22:05:57Z")
External Email - Use Caution Hey hi ! If you are interested in a good explanation of what you will find extracting features between STFT, Wavelets and eventually MP, check out this excellent paper: [http://www.jneurosci.org/content/36/12/3399.short](http://www.jneurosci.org/content/36/12/3399.short) Your regards! 2018-08-03 20:53 GMT-03:00 Mainak Jas \: >         External Email - Use Caution > > Hi, > > If you're interested in feature extraction, you might want to look into > mne-features: [API Documentation — mne\_features 0.2 documentation](https://mne-tools.github.io/mne-features/api.html). > > It should fit your needs. > > Best, > Mainak > > >         External Email - Use Caution > > > > Greetings, > > > > I use short time Fourier transform to extract features from the > > time-series EEG signals. Now I plan to use Wavelet that I don't have any > > prior experience in. > > > > Can someone provide a Python implementation (or MNE - btw, I couldn't > > digest mne morlet for some reason!)? Also, what output I should expect, and > > what features can be computed from the Wavelet transform output to be ready > > to fit a learner? > > > > A question I would also like to ask those who're concerned about feature > > extraction, why would I use Wavelet over STFT, or vice versa? > > > > Thank you, > > Badar Almarri > > Graduate Student > > Dept. of Computer Science and Engr. > > University of Connecticut > > badar.almarri at uconn.edu > > \<[http://www.linkedin.com/pub/badar-almarri/48/b09/8b8>](http://www.linkedin.com/pub/badar-almarri/48/b09/8b8>); > > > > \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ > > Mne\_analysis mailing list > > Mne\_analysis at nmr.mgh.harvard.edu > > [Mne\_analysis Info Page](https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis) > > > > The information in this e-mail is intended only for the person to whom it > > is > > addressed. If you believe this e-mail was sent to you in error and the > > e-mail > > contains patient information, please contact the Partners Compliance > > HelpLine at > > [MyComplianceReport.com: Compliance and Ethics Reporting](http://www.partners.org/complianceline) . If the e-mail was sent to you > > in error > > but does not contain patient information, please contact the sender and > > properly > > dispose of the e-mail. > > \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ > Mne\_analysis mailing list > Mne\_analysis at nmr.mgh.harvard.edu > [Mne\_analysis Info Page](https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis) > > The information in this e-mail is intended only for the person to whom it > is > addressed. If you believe this e-mail was sent to you in error and the > e-mail > contains patient information, please contact the Partners Compliance > HelpLine at > [MyComplianceReport.com: Compliance and Ethics Reporting](http://www.partners.org/complianceline) . If the e-mail was sent to you in > error > but does not contain patient information, please contact the sender and > properly > dispose of the e-mail. -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20180806/6e6ac219/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20180806/6e6ac219/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [August 7, 2018, 8:35pm UTC](https://mne.discourse.group/t/wavelet-features-extraction/1586/4 "2018-08-07T20:35:20Z")
External Email - Use Caution > If you're interested in feature extraction, you might want to look into mne-features: [API Documentation — mne\_features 0.2 documentation](https://mne-tools.github.io/mne-features/api.html). This is great! Could this library be advertised somewhere in the MNE-Python website or documentation? This would avoid people (like me 🙂 reinventing the wheel. And perhaps more generally, could we have something equivalent to scikit-learn-contrib ([https://github.com/scikit-learn-contrib/scikit-learn-contrib/blob/master/README.md](https://github.com/scikit-learn-contrib/scikit-learn-contrib/blob/master/README.md)), to collect MNE-compatible projects? Alejandro ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [August 7, 2018, 8:35pm UTC](https://mne.discourse.group/t/wavelet-features-extraction/1586/5 "2018-08-07T20:35:43Z")
External Email - Use Caution Thank you, all! Best, Badar Almarri Graduate Student Dept. of Computer Science and Engr. University of Connecticut badar.almarri at uconn.edu \<[http://www.linkedin.com/pub/badar-almarri/48/b09/8b8](http://www.linkedin.com/pub/badar-almarri/48/b09/8b8)\> ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [August 10, 2018, 9:35am UTC](https://mne.discourse.group/t/wavelet-features-extraction/1586/6 "2018-08-10T09:35:54Z")
External Email - Use Caution hi, > \> If you're interested in feature extraction, you might want to look into mne-features: [API Documentation — mne\_features 0.2 documentation](https://mne-tools.github.io/mne-features/api.html). > > This is great! Could this library be advertised somewhere in the > MNE-Python website or documentation? This would avoid people (like me > 🙂 reinventing the wheel. I did not advertise it too much so far as it's still under construction. > And perhaps more generally, could we have something equivalent to > scikit-learn-contrib > ([https://github.com/scikit-learn-contrib/scikit-learn-contrib/blob/master/README.md](https://github.com/scikit-learn-contrib/scikit-learn-contrib/blob/master/README.md)), > to collect MNE-compatible projects? yes we could do this. We probably need someone to volunteer to coordinate this. Alex