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Chunk #23 — 2. The MNE-Python standard workflow for M/EEG data analysis — 2.3. Preprocessing

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MEG and EEG data analysis with MNE-Python.
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by not modifying the original data but instead applying the projections on demand. This enables the user to explore the effects of particular SSPs later in the pipeline and to selectively abandon some projection vectors if the signals of interest are attenuated. After the above steps, one can obtain clean data as illustrated in Figure 1, which then can be further processed in epochs and evoked data, see Figure 2.