{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Compare the different ICA algorithms in MNE\n\nDifferent ICA algorithms are fit to raw MEG data, and the corresponding maps\nare displayed.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: Pierre Ablin <pierreablin@gmail.com>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from time import time\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.preprocessing import ICA\n\nprint(__doc__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Read and preprocess the data. Preprocessing consists of:\n\n- MEG channel selection\n- 1-30 Hz band-pass filter\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_path = sample.data_path()\nmeg_path = data_path / \"MEG\" / \"sample\"\nraw_fname = meg_path / \"sample_audvis_filt-0-40_raw.fif\"\n\nraw = mne.io.read_raw_fif(raw_fname).crop(0, 60).pick(\"meg\").load_data()\n\nreject = dict(mag=5e-12, grad=4000e-13)\nraw.filter(1, 30, fir_design=\"firwin\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define a function that runs ICA on the raw MEG data and plots the components\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "def run_ica(method, fit_params=None):\n    ica = ICA(\n        n_components=20,\n        method=method,\n        fit_params=fit_params,\n        max_iter=\"auto\",\n        random_state=0,\n    )\n    t0 = time()\n    ica.fit(raw, reject=reject)\n    fit_time = time() - t0\n    title = f\"ICA decomposition using {method} (took {fit_time:.1f}s)\"\n    ica.plot_components(title=title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "FastICA\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "run_ica(\"fastica\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Picard\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "run_ica(\"picard\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Infomax\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "run_ica(\"infomax\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Extended Infomax\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "run_ica(\"infomax\", fit_params=dict(extended=True))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}