# Converting Mind Monitor CSV Data from Muse-S Headband to EDF via MNE-Python

**URL:** https://mne.discourse.group/t/converting-mind-monitor-csv-data-from-muse-s-headband-to-edf-via-mne-python/9346
**Category:** Support & Discussions
**Created:** [September 22, 2024, 9:38am UTC](https://mne.discourse.group/t/converting-mind-monitor-csv-data-from-muse-s-headband-to-edf-via-mne-python/9346 "2024-09-22T09:38:22Z")
**Posts on this page:** 1
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### Author: ![mynameisjustalex](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/mynameisjustalex/32/3897_2.png) [@mynameisjustalex](https://mne.discourse.group/u/mynameisjustalex)
#### Post date: [October 3, 2024, 7:28am UTC](https://mne.discourse.group/t/converting-mind-monitor-csv-data-from-muse-s-headband-to-edf-via-mne-python/9346/3 "2024-10-03T07:28:42Z")

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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](https://github.com/NirLab-TAU/sleepeegpy)**, an open-source Python package for sleep EEG analysis that integrates **MNE-Python** and other tools.

**Our workflow involves:**

1. Collecting data from the **Muse-S headband** (exported via **Mind Monitor** in CSV format),
2. Preprocessing and converting the data to **EDF** using **MNE-Python** ,
3. 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](https://github.com/NirLab-TAU/sleepeegpy/blob/main/pyproject.toml)**, 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.

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