[Mne_analysis] Repost: alternative to decoding across time
jeanremi.king at gmail.com
Tue Jun 16 15:17:05 EDT 2020
External Email - Use Caution
You should check the CSP decoding example (as well as the pyRiemann python
package) for such purposes.
All the best
On Tue, 16 Jun 2020 at 20:07, Dirk van Moorselaar <
dirkvanmoorselaar at gmail.com> wrote:
> External Email - Use Caution
> Dear mne users,
> I am sorry if this is a repost. I sent this mail earlier in June but did
> not find it back in the mailing list so I am sending it again.
> We’ve conducted a study where we want to (using mne) differentiate between
> three mental states (thinking, feeling, and resting), entered in 12 second
> blocks (with 12 trials per condition). Ultimately we'd like to see if
> training in meditation makes these states more different (as would be
> indexed by higher classification acc.).
> Thus, for our purposes decoding along the time-course of the 12 seconds is
> largely irrelevant. We simply want the best way possible to differentiate
> between the three states, which might be better achieved by collapsing
> across time (the states are likely to be highly variable over time, i.e.,
> at any specific time points, making classification difficult).
> So, we’re seeking advice on ways to decode these conditions that might
> lend better classification than time-course decoding. Put differently, we
> would be interested in a way that enables a clumping together of the time
> We were thinking of splitting the 12s trial data into 1s epochs, as this
> would increase our power. Does anyone have experience with a similar method
> or can suggest a better option?
> Dirk van Moorselaar
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
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