eeg-analysis 0.1.3

A library for processing and plotting EEG data

EEG Analysis

This Library provides functions to process and plot EEG data, specifically focusing on the effects of different states (Before, During, After) on various EEG frequency bands.

Installation

To install the library, use pip:

pip install eeg_analysis

Usage

Loading the Data

First, load the data from a CSV file. The CSV file should be structured with columns for subject, location, band, and power measurements for three states (Before, During, After).

import pandas as pd
from eeg_analysis import load_data

# Load the data
data = load_data('path/to/your/data.csv')

Cleaning the Data

Once the data is loaded, it needs to be cleaned and structured into a more usable format. The clean_data function organizes the data into a DataFrame with appropriate columns.

from eeg_analysis import clean_data

# Clean and structure the data
df = clean_data(data)

# Optionally, save the cleaned data to a new CSV file
df.to_csv('cleaned_data.csv', index=False)
print(df.head())

Performing Paired T-Tests

The library includes a function to perform paired t-tests between the different states for a specified frequency band. This can help determine if there are statistically significant differences in power between the states.

from eeg_analysis import perform_ttests

# Perform t-tests for a specific band (e.g., 'Delta')
ttest_results_df = perform_ttests(df, 'Delta')

# Display the results
print(ttest_results_df)

Plotting Median Power Across Bands

The library provides a function to plot the median power across different states for each frequency band. This can help visualize the changes in power for different subjects and states.

from my_pypi_library import plot_median_across_bands

# Plot the median power across bands
plot_median_across_bands(df)

Example Workflow

Here's a complete example that demonstrates the typical workflow using the library:

import pandas as pd
from eeg_analysis import load_data, clean_data, perform_ttests, plot_median_across_bands

# Step 1: Load the data
data = load_data('path/to/your/data.csv')

# Step 2: Clean and structure the data
df = clean_data(data)

# Step 3: Perform t-tests for a specific band (e.g., 'Delta')
ttest_results_df = perform_ttests(df, 'Delta')
print(ttest_results_df)

# Step 4: Plot the median power across bands
plot_median_across_bands(df)

API Reference

load_data(filepath: str) -> pd.DataFrame

Load the data from a CSV file.

• Parameters:

  • filepath (str): The path to the CSV file containing the data.

• Returns:

  • pd.DataFrame: A DataFrame containing the loaded data.

clean_data(data: pd.DataFrame) -> pd.DataFrame

Clean and structure the data into a usable format.

• Parameters:

  • data (pd.DataFrame): The raw data DataFrame.

• Returns:

  • pd.DataFrame: A cleaned and structured DataFrame.

perform_ttests(data: pd.DataFrame, band: str) -> pd.DataFrame

Perform paired t-tests for a given frequency band.

• Parameters:

  • data (pd.DataFrame): The cleaned data DataFrame.
  • band (str): The frequency band to analyze (e.g., 'Delta').

• Returns:

  • pd.DataFrame: A DataFrame containing the t-test results for each subject and comparison.

plot_median_across_bands(data: pd.DataFrame)

Plot the median power across different states for each frequency band.

• Parameters:
  • data (pd.DataFrame): The cleaned data DataFrame.

Contributing

Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for details.