Base_Concepts.ipynb

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   "source": [
    "- 包含写代码过程中的一些定义的基本含义\n",
    "- 包含基线漂移去除的代码"
   ]
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    "Define the baseline period:\n",
    ">  None or tuple of length 2 (default (None, 0))\n",
    "The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. Correction is applied by computing mean of the baseline period and subtracting it from the data. The baseline (a, b) includes both endpoints, i.e. all timepoints t such that a <= t <= b."
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   "source": [
    "baseline = (None,0) # means from the first instant to t = 0"
   ]
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   "source": [
    "Read epochs and baseline correction\n",
    "- https://github.com/mne-tools/mne-python/blob/master/mne/baseline.py\n",
    "- https://stackoverflow.com/questions/29156532/python-baseline-correction-library"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define epochs parameters:"
   ]
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   "source": [
    "event_id = dict(aud_l=1, aud_r=2)  # event trigger and conditions\n",
    "tmin = -0.2  # start of each epoch (200ms before the trigger)\n",
    "tmax = 0.5  # end of each epoch (500ms after the trigger)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Alternatively one can restrict to magnetometers or gradiometers with:"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "mag_picks = mne.pick_types(raw.info, meg='mag', eog=True, exclude='bads')\n",
    "grad_picks = mne.pick_types(raw.info, meg='grad', eog=True, exclude='bads')"
   ]
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   "source": [
    "epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,\n",
    "                    baseline=baseline, preload=False, reject=reject)\n",
    "print(epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# https://mne-tools.github.io/stable/generated/mne.Epochs.html#mne.Epochs.apply_baseline\n",
    "apply_baseline() #Baseline correct epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define peak-to-peak rejection parameters for gradiometers, magnetometers and EOG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get single epochs for one condition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs_data = epochs['aud_l'].get_data()\n",
    "print(epochs_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute evoked responses for auditory responses by averaging and plot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evoked = epochs['aud_l'].average()\n",
    "print(evoked)\n",
    "evoked.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### reference:\n",
    "- https://mne-tools.github.io/0.11/auto_tutorials/plot_introduction.html"
   ]
  }
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