# Processing EEG with MNE-Python

```python
import mne
import warnings
import numpy as np
import matplotlib.pyplot as plt

from almirah import Layout

mne.set_log_level(False)
warnings.filterwarnings('ignore')
```

```python
lay = Layout(root="/path/to/data", specification_name="bids")
lay
```

    <Layout root: '/path/to/data'>
    
```python
lay = Layout.get(specification_name='bids')
```

```python
files = lay.query(datatype="eeg", extension=".vhdr")
len(files)
```

    2223

```python
vhdr_file = lay.query(subject="D0019", session="101", datatype="eeg", task="rest", extension =".vhdr")[0]
eeg_file = lay.query(subject="D0019", session="101", datatype="eeg", task="rest", extension=".eeg")[0]
montage_file = lay.query(subject="D0019", session="101", space="CapTrak", suffix="electrodes")[0]

print(vhdr_file.rel_path)
```

    sub-D0019/ses-101/eeg/sub-D0019_ses-101_task-rest_run-01_eeg.vhdr

```python
eeg_file.download()
```

```python
montage = mne.channels.read_custom_montage(montage_file.path)
raw = mne.io.read_raw_brainvision(vhdr_file.path, preload=True)
raw.set_montage(montage)
raw.info["bads"] = ["VREF"]

raw.info
```

<details open>
    <summary><strong>General</strong></summary>
    <table class="table table-hover table-striped table-sm table-responsive small">
	<tr>
	    <th>Measurement date</th>
	    <td>Unknown</td>
	</tr>
	<tr>
	    <th>Experimenter</th>
	    <td>Unknown</td>
	</tr>
	<tr>
	    <th>Participant</th>
	    <td>Unknown</td>
	</tr>
    </table>
</details>
<details open>
    <summary><strong>Channels</strong></summary>
    <table class="table table-hover table-striped table-sm table-responsive small">
	<tr>
	    <th>Digitized points</th>
	    <td>132 points</td>
	</tr>
	<tr>
	    <th>Good channels</th>
	    <td>128 EEG, 5 misc</td>
	</tr>
	<tr>
	    <th>Bad channels</th>
	    <td>VREF</td>
	</tr>
	<tr>
	    <th>EOG channels</th>
	    <td>Not available</td>
	</tr>
	<tr>
	    <th>ECG channels</th>
	    <td>Not available</td>
	</tr>
    </table>
</details>
<details open>
    <summary><strong>Data</strong></summary>
    <table class="table table-hover table-striped table-sm table-responsive small">
	<tr>
	    <th>Sampling frequency</th>
	    <td>1000.00 Hz</td>
	</tr>
	<tr>
	    <th>Highpass</th>
	    <td>0.00 Hz</td>
	</tr>
	<tr>
	    <th>Lowpass</th>
	    <td>500.00 Hz</td>
	</tr>
    </table>
</details>

# Preprocessing

```python
# Apply a band-pass filter
raw.filter(1, 40, fir_design="firwin")

# Plot the raw data
raw.plot(n_channels=30, duration=30, scalings="auto")
plt.close()
```

![png](../images/eeg/raw-plot.png)
    
# Artifact removal using ICA

```python
# Setup ICA
ica = mne.preprocessing.ICA(n_components=15, random_state=97)
ica.fit(raw)

# Plot ICA components
ica.plot_components()

# Select bad components manually (usually by visual inspection)
ica.exclude = [0, 6, 14]  # Example: components 0 and 1 are eye blinks and heartbeats

# Apply ICA removal
raw_clean = ica.apply(raw.copy())
```
    
![png](../images/eeg/ica-plot.png)
    
# Power Spectral Analysis

```python
# Compute and plot Power Spectral Density (PSD)
raw_clean.compute_psd().plot(exclude="bads", amplitude=False)
plt.show()
```
    
![png](../images/eeg/psd-plot.png)
    

```python
raw_clean.compute_psd().plot_topomap(ch_type="eeg", agg_fun=np.median)
plt.close()
```
    
![png](../images/eeg/spectrum-topo-plot.png)