Chapter 2: Loading and Inspecting Data¶
Continuing from Chapter 1 — Installation and First Launch.
This chapter loads a recording into MNELAB and tours the panels that describe it — the same sample_audvis_raw.fif file the MNE-Python track's own Loading and Inspecting Data chapter already has you download to ~/mne_data, so there's nothing new to fetch.
Opening a recording¶
Drag the .fif file onto the main window, or use File → Open.... Beyond the formats MNE-Python itself reads (FIF, EDF/BDF, BrainVision, EEGLAB .set, and more), MNELAB adds a few importers of its own — most notably extensive support for XDF, a format used for recording multiple synchronized data streams (EEG plus markers plus, say, eye-tracking) that plain MNE-Python doesn't handle as conveniently, plus MATLAB, NumPy, and BVR files. Once loaded, the recording appears as a new row in the sidebar.
The sidebar and info panel¶
The sidebar lists every dataset you have open — you can load several recordings at once and switch between them by clicking a row. Selecting a dataset fills the info panel next to it with a summary: number and types of channels, sampling frequency, duration, size in memory and on disk, plus rows for events, annotations, montage, and reference. Several of these rows are clickable and jump straight to the relevant editor — clicking Reference opens the same dialog Chapter 3 uses, clicking Events opens the one Chapter 5 uses, and so on.
Looking at the data¶
- Plot → Plot Data opens a scrolling signal browser in its own window — the everyday way to eyeball a recording for obvious problems before doing anything else.
- Plot → Plot PSD calculates and plots the power spectral density, useful for spotting line noise or an unusually noisy channel.
- Plot → Plot Channel Locations draws the montage in 2D/3D once one is set (covered next chapter), confirming every channel landed in a sensible spot.
If you'd rather see this same import-and-inspect step as code, it's exactly the mne.io.read_raw_fif() call plus raw.info, raw.plot(), and raw.plot_psd() covered in the MNE-Python track's Loading and Inspecting Data chapter — Chapter 7 of this guide shows exactly how MNELAB gets you from one to the other.