You need to run the above commands every time you start a new session in the terminal. 1. Load the raw MEG data and plot it. Illustrate with a screenshot. In [#]: raw=mne.io.read_raw_fif('R2218_two-tones-raw.fif') In [#]: raw.load_data() In [#]: raw.plot() The raw data should now appear on the screen. This is the raw data recorded during the localizer run. It was denoised using the de Cheveigne/Simon algorithm in Matlab and then converted from the native .sqd format used by the UMD system to the .fif format assumed by the MNE software. When you record data for the class project, But for right now we won’t worry about them so you’ll need to do these extra steps. you can focus on getting comfortable with MEG data. Toggle through the selections to examine the data in all the sensors. a) How many sensors does the UMD system have in total? b) What sensor is carrying information about the timing of the auditory stimuli? c) The blue lines are the MEG data recorded at different sensors. Eyeblinks create big peaks in the data due to the electrical activity created by muscle movement, and these are of much greater amplitude than neural activity (therefore they need to be excluded from data analysis to increase signal-to-noise ratio). Try to identify some eyeblinks in the data and take a screen shot. d) Scroll the data in time with the cursors at the bottom. Do you notice temporal regularities in the data? Include a screenshot(s) for illustration. Do you have hypotheses about the source(s) of the effect(s)? e) Anything else you find interesting about the data? 2. Close the plot and now filter the data: a) Change Lowpass filter value to 10: In [#]: raw_10 = raw.filter(l_freq=None, h_freq=10.0) In [#]: raw_10.plot() How does the data change? Illustrate with a screenshot. b) Change Lowpass filter value to 40, which is a good default.
You need to run the above commands every time you start a new session in the terminal. 1. Load the raw MEG data and plot it. Illustrate with a screenshot. In [#]: raw=mne.io.read_raw_fif('R2218_two-tones-raw.fif') In [#]: raw.load_data() In [#]: raw.plot() The raw data should now appear on the screen. This is the raw data recorded during the localizer run. It was denoised using the de Cheveigne/Simon algorithm in Matlab and then converted from the native .sqd format used by the UMD system to the .fif format assumed by the MNE software. When you record data for the class project, you’ll need to do these extra steps. But for right now we won’t worry about them so you can focus on getting comfortable with MEG data. Toggle through the selections to examine the data in all the sensors. a) How many sensors does the UMD system have in total? b) What sensor is carrying information about the timing of the auditory stimuli? c) The blue lines are the MEG data recorded at different sensors. Eyeblinks create big peaks in the data due to the electrical activity created by muscle movement, and these are of much greater amplitude than neural activity (therefore they need to be excluded from data analysis to increase signal-to-noise ratio). Try to identify some eyeblinks in the data and take a screen shot. d) Scroll the data in time with the cursors at the bottom. Do you notice temporal regularities in the data? Include a screenshot(s) for illustration. Do you have hypotheses about the source(s) of the effect(s)? e) Anything else you find interesting about the data? 2. Close the plot and now filter the data: a) Change Lowpass filter value to 10: In [#]: raw_10 = raw.filter(l_freq=None, h_freq=10.0) In [#]: raw_10.plot() How does the data change? Illustrate with a screenshot. b) Change Lowpass filter value to 40, which is a good default.