Origin, significance, and interpretation of EEG

[@SubhashPratap]

Well explained, Your way of teaching is excellent. Many Thanks .

[@marziyehzarei3278]

I wish I had met this channel earlier. it is very helpful. Thanks a lot 🌹

[@mikexcohen1]

You're welcome 😊

[@dipankardey1044]

very clear to a novice in computational neuroscience. Thanks a ton for your videos

[@mikexcohen1]

Most welcome!

[@user-js5jf4to3f]

Thank you for this video. It is very well explained! One thing I am still confused is related to interpreting the amplitude of ERP charts. Does negative amplitudes mean higher negative extracellular activity that can be interpreted as more neurons firing? And is that translated as a more red area in topography maps? Or how should I interpret the negative and positive amplitudes in ERPs? Thank you!

[@mikexcohen1]

The sign of the potential is hard to interpret from the scalp. In theory, the sign of the extracellular potential indicates the flow of ions into vs. out of a neuron, but at a macroscopic scale with cortical folding, it's impossible to know with certainty whether a negative potential indicates flow into vs out of the neurons given the uncertainties about whether the sources are gyral or sulcal. Furthermore, data processing steps like referencing and filtering can change the sign of a potential.

[@EEGucation]

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.

[@mikexcohen1]

That's an old screenshot, but I believe the reference was linked mastoids.

[@tobybromfield3664]

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.

[@mikhailreshetnikov3236]

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.

[@tuongminhquoc]

I really love your videos! Thank you!

[@mikexcohen1]

You're welcome!

[@muhammadadeelkhan3188]

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!

[@mikexcohen1]

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.

[@sivatejaswi5484]

Sir will you please provide data sets for EEG signals. It is very much use full for my research .

[@mikexcohen1]

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.

[@sivatejaswi5484]

Thank you sir 🙏

[@mikexcohen1]

All the best, kind internet stranger.

[@mojganehsanifard4876]

amazing! thank you

[@andrassarkozy1157]

anyonen please could share the actual sample dataset what he is working with? THX!

[@mikexcohen1]

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.

[@sirabhop.s]

Thank you

[@TheKrzyniu]

Thank you!!!