Raphael Vallat

Hi @jftsang, Unfortunately, I don't think there's anything that we can do to fix this. Numba allows to greatly improve the computation time of some functions in antropy, and the...

Hi @jftsang, I believe you can set up the `export NUMBA_DISABLE_JIT=1` environment variable to disable JIT (see https://numba.pydata.org/numba-doc/dev/reference/envvars.html).

Hi @zixiao-yin, thank you :-) For now only a single EEG channel is supported, preferentially C4-M1 or C3-M2. Adding support for multiple EEG channels in the core algorithm should be...

Hi @zixiao-yin, You should be able to detect k-complexes with the yasa.sw_detect function. Make sure that you limit the detection to N2 sleep. Detection of sharp waves is not implemented....

Thanks @umair-hassan — I think this is a great idea. It will be better than having to re-train a separate classifier for _2, 3, ... n_ EEG channels, both in...

**Update**: implementing the above solutions 1 and 2 does not make a noticeable difference in computation time. The two parameters that really impact the computation time (apart from number of...

Thank you @Pierre-Bartet, appreciate it! I agree that it would be a nice feature, i.e. detecting disconnected / flat or very noisy channels, not only for sleep staging but as...

Not epochs, but we do have some markers of overall EEG signal quality (e.g. https://sleepdata.org/datasets/mesa/variables/quo2m15). However, I think I'd prefer to use a more heuristic approach for this one and...

Hi @skjerns, Yeah, sorry about that, I really don't have any time to work on Visbrain these days. But we should accept & merge the current PR. I'm not an...

Hi @Dolki ! 1) Thanks for noting the issue with the frequency. In fact, what's happening under the hood is that Visbrain is automatically detecting the peak frequency in the...