info/tutorials/multimodal/average-eeg-across-disorders.md

Average EEG signal across various disorders

import mne
import pandas as pd
import seaborn as sns

from almirah import Dataset

mne.set_log_level(False)

ds = Dataset(name="calm-brain")
eeg_header_files = ds.query(datatype="eeg", task="rest", extension=".vhdr")
eeg_data_files = ds.query(datatype="eeg", task="rest", extension=".eeg")
len(eeg_data_files)

1120

for file in eeg_data_files:
    file.download()

db = ds.components[2]
db.connect("username", "password")
df = ds.query(table="presenting_disorders")
df[["subject", "session", "addiction"]].head()

	subject	session	addiction
0	D0019	101	0
1	D0019	111	0
2	D0020	101	0
3	D0020	111	<NA>
4	D0021	101	0

def get_eeg_mean(file):
    raw = mne.io.read_raw_brainvision(file.path)
    return raw.get_data().mean()

def get_disorders(file):
    disorders = []
    subject, session = file.tags["subject"], file.tags["session"]
    filtered_df = df[(df["subject"] == subject) & (df["session"] == session)]

    if filtered_df.empty:
        print(subject, session)
        return None
    
    for column in ["addiction", "bipolar", "dementia", "ocd", "schizophrenia"]:
        presence = filtered_df.iloc[0][column]
        if not pd.isna(presence) and presence:
            disorders.append(column)
    
    return disorders if disorders else ["healthy"]

def file_func(file):
    mean_eeg, disorders = get_eeg_mean(file), get_disorders(file)

    if not disorders:
        return pd.DataFrame()
        
    mean_df = pd.DataFrame({"mean": [mean_eeg] * len(disorders), "disorder": disorders})
    return mean_df.dropna()

mean_dfs = list(map(file_func, eeg_header_files))
mean_dfs = [df for df in mean_dfs if not df.empty]
mean_df = pd.concat(mean_dfs, sort=False)
mean_df.head()

	mean	disorder
0	-0.008766	healthy
1	0.000457	addiction
2	-0.006335	healthy
3	-0.002764	healthy
4	-0.008269	ocd

mean_df.groupby("disorder").mean()

	mean
disorder
addiction	0.003414
bipolar	0.001613
dementia	0.010485
healthy	0.002449
ocd	-0.000875
schizophrenia	0.005444

ax = sns.violinplot(data=mean_df, x="mean", hue="disorder")
sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))