I enjoyed the lecture. I suspect you used an active reference on the EEG sample shown. The slow waves are identical on all channels probably related to contamination from the referential electrode.
Thank you very much for your videos! I believe there's a misunderstanding on the EEG advantages slide, as you said that in your lecture EEG = MEG, although "temporal resolution and precision" is an advantage of EEG, whereas MEG has the spatial resolution advantage.
In a other video you said you mentioned phase locking and evoked and induced activity ? I didn't find this. I'm very confused on the meanings and differences between these concepts.
Thanks Mike for the nice explanations. I am curious that this course will cover the ERP and ERSP analysis or not? Besides that kindly recommend me the Machine Learning course that is most related to the physiological data. Thanks in advance!
ERSP yes. Much of the course is focused on spectral and time-frequency analyses. I do cover ERPs briefly, but only in terms of computing them, not in terms of interpreting the peaks.
Hi Andras. I do share the data I use for teaching, but the screenshots in this video are just for illustration. I made these slides a long time ago and don't even know which data I used... anyway, you can check out my ANTS video series, and the data and code are on my github site.
Well explained, Your way of teaching is excellent. Many Thanks .
I wish I had met this channel earlier. it is very helpful. Thanks a lot 🌹
You're welcome 😊
very clear to a novice in computational neuroscience. Thanks a ton for your videos
Most welcome!
I enjoyed the lecture. I suspect you used an active reference on the EEG sample shown. The slow waves are identical on all channels probably related to contamination from the referential electrode.
That's an old screenshot, but I believe the reference was linked mastoids.
Thank you very much for your videos!
I believe there's a misunderstanding on the EEG advantages slide, as you said that in your lecture EEG = MEG, although "temporal resolution and precision" is an advantage of EEG, whereas MEG has the spatial resolution advantage.
In a other video you said you mentioned phase locking and evoked and induced activity ? I didn't find this. I'm very confused on the meanings and differences between these concepts.
Thanks Mike for the nice explanations. I am curious that this course will cover the ERP and ERSP analysis or not? Besides that kindly recommend me the Machine Learning course that is most related to the physiological data. Thanks in advance!
ERSP yes. Much of the course is focused on spectral and time-frequency analyses. I do cover ERPs briefly, but only in terms of computing them, not in terms of interpreting the peaks.
I really love your videos! Thank you!
You're welcome!
Thank you sir 🙏
All the best, kind internet stranger.
Sir will you please provide data sets for EEG signals. It is very much use full for my research .
I provide a few for my books and courses. But you can get tons of free EEG data from online data repositories. Some googling will lead you to them.
amazing! thank you
Thank you
anyonen please could share the actual sample dataset what he is working with? THX!
Hi Andras. I do share the data I use for teaching, but the screenshots in this video are just for illustration. I made these slides a long time ago and don't even know which data I used... anyway, you can check out my ANTS video series, and the data and code are on my github site.
Thank you!!!