Hi Clemens,
Thank you for your response on converting CSV data to EDF using RawArray and edfio! This clarified some of the steps we need to take for the conversion process.
To give you some context, we are using SleepEEGpy, an open-source Python package for sleep EEG analysis that integrates MNE-Python and other tools.
Our workflow involves:
- Collecting data from the Muse-S headband (exported via Mind Monitor in CSV format),
- Preprocessing and converting the data to EDF using MNE-Python,
- Performing sleep staging and event detection with SleepEEGpy and YASA.
We’re working with single-channel EEG data from the AF7 or AF8 electrodes and are still relatively new to these tools.
Follow-up Questions:
1.For reference, we’ve checked SleepEEGpy’s dependencies, but want to ensure if we can use more up to date versions of MNE-Python without it affecting the procedures provided by SleepEEGpy’s documentation
2.Handling Consumer-Grade EEG Data: Given that we’re using consumer-grade EEG data from the Muse-S (which may have more noise or lower resolution compared to research-grade devices), do you have any recommendations on how to best handle or preprocess this type of data within MNE-Python? Are there specific limitations or adjustments we should be aware of when using SleepEEGpy for analysis?
We’d appreciate any advice on managing these dependencies and ensuring compatibility within our Python environment for sleep EEG analysis.