Feasibility of Using SleepEEGpy with Consumer-Grade Headband Muse-S EEG Data for Sleep Analyses

[mynameisjustalex]

Hi SleepEEGpy Community,

Me and my team at university are currently working on a research project that involves analyzing EEG data collected with the Muse-S headband (with AF7, AF8, TP9, TP10 channels), exporting the raw data via the MindMonitor app in CSV format. The Muse-S is a consumer-grade device that streams data from 4 channels at 256 Hz.

We are particularly interested in using SleepEEGpy for the following analyses:

• Sleep staging (N1, N2, N3, REM stages)
• Detection of sleep spindles
• Identification of K-complexes
• Other indices like REM density and spectral analysis

I understand that SleepEEGpy integrates several powerful tools like MNE-Python, YASA, and SpecParam (formerly FOOOF) for sleep EEG analysis. However, as I’m working with consumer-grade data (Muse-S), which is quite different from high-density EEG data, I’m wondering:

1. Is SleepEEGpy (with MNE-Python, YASA, SpecParam) suitable for processing consumer-grade EEG data from the Muse-S, especially for performing sleep staging (using single-channel EEG from either AF7 or AF8) and extracting sleep indices like spindles, K-complexes, and REM density? I understand that SleepEEGpy was designed for high-density EEG data, so any insights on whether it can be adapted for consumer EEG data would be greatly appreciated.

2. If it’s possible to use SleepEEGpy for this type of data, I would be grateful for any general suggestions on how to approach the preprocessing and analysis. We have limited manpower and time for this project, so any guidance (e.g., handling CSV to EDF/XDF conversion from the mindmonitor app, key considerations for preprocessing, etc.) would be much appreciated.

I understand this is a detailed question, especially given the nature of consumer-grade EEG data, and I truly appreciate any advice or suggestions the community can offer. Thank you in advance for your time, and I look forward to any insights you might be able to share!