-
Notifications
You must be signed in to change notification settings - Fork 124
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Adding pipelines for cli analysis (#202)
* started pipelines function * almost working simple function equivalents of nb scripts * fix: fixed import (brainflow updated API) * sqc fixes for unicorn (#176) * Ignore pushes * Trying to create a cli * Stepping through the problem * First commit * Fixing pause in signal quality check * Fixing Signal quality check problem * fix the technical debt * Save path done for automated saving pdf * I feel amazing * Almost through * Update eegnb/cli/__main__.py Co-authored-by: Erik Bjäreholt <erik@bjareho.lt> * Trying to create cli but it's being really painful * Extra word cli error * Changed example handling * Pain * Adding whole datapath * Finally fixed cli * hmm * Looking good * added hyperlink * Having some issues with detecting css and image deltetion * Just the css now * Fixed the css linking problem though it's a weird soln * Automated running, still fnames problem * Hahahah embedded images in html * Improving code * Okay now * Look at that * Almost there just the two figures now * Now * Added attrdict to do with cli error Co-authored-by: John Griffiths <j.davidgriffiths@gmail.com> Co-authored-by: Erik Bjäreholt <erik@bjareho.lt> Co-authored-by: John Griffiths <JohnGriffiths@users.noreply.github.com>
- Loading branch information
1 parent
368afa3
commit 6714740
Showing
12 changed files
with
641 additions
and
41 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,39 @@ | ||
<!DOCTYPE html> | ||
<html> | ||
<head> | ||
<link href="styling.css" rel="stylesheet" /> | ||
<title>Analysis Report</title> | ||
</head> | ||
<body> | ||
<div class="topnav"> | ||
<a href="#Description">Description</a> | ||
<a href="#Raw Epoch">Raw Epoch</a> | ||
<a href="#Stimulus Response">Stimulus Response</a> | ||
</div> | ||
<div id="Description"> | ||
<h1>Analysis Report</h1> | ||
<p> | ||
<b></b>Experiment Name: {} <br> | ||
Subject Id: {} <br> | ||
Session Id: {} <br> | ||
EEG Device: {} <br> | ||
Drop Percentage: {} <br> <br> | ||
This is an analysis report for the experiment. <br> For more information about the experiment, please visit the <a href="https://neurotechx.github.io/eeg-notebooks/">documentation</a> | ||
</p> | ||
</div> | ||
<div id="Raw Epoch"> | ||
<h2>Raw Epoch</h2> | ||
<p> | ||
The raw epoch is shown below. The raw epoch is the data that is recorded from the EEG headset. The raw epoch is then processed to remove noise and artifacts. | ||
</p> | ||
<img src="power_spectrum.png" alt="Raw Epoch" /> | ||
</div> | ||
<div id="Stimulus Response"> | ||
<h2>Stimulus Response</h2> | ||
<p> | ||
The stimulus response is shown below. The stimulus response is the data that is recorded from the EEG headset after removing noise and artifacts. | ||
</p> | ||
<img src="erp_plot.png" alt="Stimulus Response" /> | ||
</div> | ||
</body> | ||
</html> |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,95 @@ | ||
|
||
# Generating html using Python | ||
|
||
from airium import Airium | ||
from typing import Dict | ||
import os | ||
import eegnb | ||
import base64 | ||
|
||
a = Airium() | ||
|
||
def get_experiment_information(experiment:str): | ||
analysis_save_path = os.path.join(os.path.dirname(eegnb.__file__), "analysis") | ||
file_path = os.path.join(analysis_save_path, "experiment_descriptions") | ||
|
||
with open(os.path.join(file_path, experiment + ".txt"), 'r') as f: | ||
experiment_text = f.readlines() | ||
|
||
return experiment_text | ||
|
||
def get_img_string(image_save_path): | ||
""" Returns image as string to embed into the html report """ | ||
return base64.b64encode(open(image_save_path, "rb").read()).decode() | ||
|
||
def get_html(experimental_parameters: Dict): | ||
|
||
# add variable to store the link | ||
analysis_save_path = os.path.join(os.path.dirname(eegnb.__file__), "analysis") | ||
css_path = os.path.join(analysis_save_path, "styling.css") | ||
eeg_device, experiment, subject, session, example, drop_percentage, epochs_chosen = experimental_parameters.values() | ||
|
||
erp_image_path = os.path.join(os.getcwd(), "erp_plot.png") | ||
pos_image_path = os.path.join(os.getcwd(), "power_spectrum.png") | ||
|
||
experiment_text = get_experiment_information(experiment) | ||
|
||
|
||
""" Possibility of unique experiment text - decision to be made """ | ||
#experiment_text = "" | ||
#with open('experiment_descriptions/{}.txt'.format(experiment), 'r') as f: | ||
# experiment_text = f.readlines() | ||
|
||
a('<!DOCTYPE html>') | ||
with a.html(): | ||
with a.head(): | ||
a.link(href=css_path, rel='stylesheet', type="text/css") | ||
a.title(_t="Analysis Report") | ||
|
||
with a.body(): | ||
|
||
# Navigation bar | ||
with a.div(klass="topnav"): | ||
a.a(_t="Description", href="#Description") | ||
a.a(_t="Raw Epoch", href="#Raw Epoch") | ||
a.a(_t="Stimulus Response", href="#Stimulus Response") | ||
|
||
# Description | ||
with a.div(id="Description"): | ||
a.h1(_t="Analysis Report") | ||
with a.p(): | ||
a("Experiment Name: {} <br>".format(experiment)) | ||
|
||
if example: | ||
a("Example File <br>") | ||
else: | ||
a("Subject Id: {} <br>".format(subject)) | ||
a("Session Id: {} <br>".format(session)) | ||
|
||
a("EEG Device: {} <br>".format(eeg_device)) | ||
a('This is an analysis report for the experiment. <br> For more information about the experiment, please visit the <a href="https://neurotechx.github.io/eeg-notebooks/">documentation</a><br><br>') | ||
a("{}<br>".format(experiment_text[0])) | ||
a("{}<br>".format(experiment_text[1])) | ||
|
||
# Raw Epoch | ||
with a.div(id="Raw Epoch"): | ||
a.h2(_t="Raw Epoch") | ||
with a.p(): | ||
a("The power spectrum of the raw epoch is displayed below. The raw epoch is then processed to remove noise and artifacts.") | ||
a.img(src="data:image/png;base64, {}".format(get_img_string(pos_image_path)), alt="Raw Epoch") | ||
|
||
# Stimulus Response | ||
with a.div(id="Stimulus Response"): | ||
a.h2(_t="Stimulus Response") | ||
with a.p(): | ||
a("The stimulus response is shown below. The stimulus response is the amplitude response at the specific timescales where the response to the stimulus can be detected. <br>") | ||
a("Epochs chosen: {} <br>".format(epochs_chosen)) | ||
a("Drop Percentage: {} %<br> <br>".format(round(drop_percentage,2))) | ||
a.img(src="data:image/png;base64, {}".format(get_img_string(erp_image_path)), alt="Stimulus Response") | ||
|
||
# Delete the images | ||
os.remove(erp_image_path) | ||
os.remove(pos_image_path) | ||
|
||
# Return the html | ||
return str(a) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,2 @@ | ||
The N170 is a large negative event-related potential (ERP) component that occurs after the detection of faces, but not objects, scrambled faces, or other body parts such as hands. | ||
In the experiment we aim to detect the N170 using faces and houses as our stimuli. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,2 @@ | ||
The P300 is a positive event-related potential (ERP) that occurs around 300ms after perceiving a novel or unexpected stimulus. It is most commonly elicited through ‘oddball’ experimental paradigms, where a certain subtype of stimulus is presented rarely amidst a background of another more common type of stimulus. | ||
In the experiment, we aimed to elicit P300 response using a visual oddball stimulation. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,243 @@ | ||
""" | ||
CLI Pipeline for Analysis of EEGNB Recorded Data | ||
To do: | ||
1. Beautify analysis pdf | ||
2. Handle cli automated errors for report creation | ||
Usage: | ||
For Recorded Data: | ||
from eegnb.analysis.pipelines import create_analysis_report() | ||
create_analysis_report(experiment, eegdevice, subject, session, filepath)s | ||
For Example Datasets: | ||
from eegnb.analysis.pipelines import example_analysis_report() | ||
example_analysis_report() | ||
""" | ||
|
||
# Some standard pythonic imports | ||
import os | ||
from collections import OrderedDict | ||
import warnings | ||
import matplotlib.pyplot as plt | ||
from datetime import datetime | ||
import numpy as np | ||
from typing import Dict | ||
|
||
warnings.filterwarnings('ignore') | ||
|
||
# MNE functions | ||
from mne import Epochs,find_events, create_info | ||
from mne.io import RawArray | ||
|
||
# EEG-Notebooks functions | ||
from eegnb import generate_save_fn | ||
from eegnb.analysis.utils import load_data,plot_conditions, load_csv_as_raw, fix_musemissinglines | ||
from eegnb.analysis.analysis_report import get_html | ||
from eegnb.datasets import fetch_dataset | ||
from eegnb.devices.utils import EEG_INDICES, SAMPLE_FREQS | ||
from pathlib import Path | ||
|
||
DATA_DIR = os.path.join(os.path.expanduser("~/"), ".eegnb", "data") | ||
eegdevice, experiment_name, subject_id, session_nb, example_flag = None, None, None, None, False | ||
|
||
def load_eeg_data(experiment, subject=1, session=1, device_name='muse2016_bfn', tmin=-0.1, tmax=0.6, baseline=None, | ||
reject={'eeg': 5e-5}, preload=True, verbose=1, | ||
picks=[0,1,2,3], event_id = OrderedDict(House=1,Face=2), fnames=None, example=False): | ||
""" | ||
Loads EEG data from the specified experiment, subject, session, and device. | ||
Returns the raw and epochs objects. | ||
Procedure | ||
1. Loads the data using file names and retrives if not already present | ||
2. Epochs the data | ||
3. Computes the ERP | ||
4. Returns the raw and ERP objects | ||
Parameters | ||
---------- | ||
experiment : Experiment Name | ||
subject : Subject ID of performed experiment | ||
session : Session ID of performed experiment | ||
device_name : Device used for performed experiment | ||
tmin : Start time of the epochs in seconds, relative to the time-locked event. | ||
tmax : End time of the epochs in seconds, relative to the time-locked event. | ||
baseline : Not very sure..? | ||
reject : Rejection parameters for the epochs. | ||
preload : If True, preload the epochs into memory. | ||
verbose : If True, print out messages. | ||
picks : Channels to include in the analysis. | ||
event_id : Dictionary of event_id's for the epochs | ||
fnames : File names of the experiment data, if not passed, example files are used | ||
""" | ||
|
||
# If not using the example dataset, load the data from the specified experiment using load_csv_as_raw | ||
if not example: | ||
|
||
# Obataining the specific parameters to load the data into MNE object | ||
sfreq = SAMPLE_FREQS[device_name] | ||
ch_ind = EEG_INDICES[device_name] | ||
|
||
# Generate file names if not passed | ||
if fnames is None: | ||
raw = load_data(subject_id=subject, session_nb=session, experiment=experiment, device_name=device_name, site="local", data_dir=os.path.join(os.path.expanduser('~/'),'.eegnb', 'data')) | ||
|
||
else: | ||
# Replace Ch names has arbitarily been set to None | ||
if device_name in ["muse2016", "muse2", "museS"]: | ||
raw = load_csv_as_raw([fnames], sfreq=sfreq, ch_ind=ch_ind, aux_ind=[5], replace_ch_names=None, verbose=verbose) | ||
else: | ||
raw = load_csv_as_raw([fnames], sfreq=sfreq, ch_ind=ch_ind, replace_ch_names=None, verbose=verbose) | ||
|
||
# Getting the subject and session | ||
subject, session = fnames.split('_')[1], fnames.split('_')[2] | ||
|
||
# If using the example dataset, load the data from the example dataset | ||
else: | ||
subject, session = 1, 1 | ||
|
||
# Loading Data | ||
eegnb_data_path = os.path.join(os.path.expanduser('~/'),'.eegnb', 'data') | ||
experiment_data_path = os.path.join(eegnb_data_path, experiment, 'eegnb_examples') | ||
|
||
# If dataset hasn't been downloaded yet, download it | ||
if not os.path.isdir(experiment_data_path): | ||
fetch_dataset(data_dir=eegnb_data_path, experiment=experiment, site='eegnb_examples') | ||
|
||
raw = load_data(1,1, | ||
experiment=experiment, site='eegnb_examples', device_name=device_name, | ||
data_dir = eegnb_data_path) | ||
|
||
# Filtering the data under a certain frequency range | ||
raw.filter(1,30, method='iir') | ||
|
||
# Visualising the power spectrum | ||
fig = raw.plot_psd(fmin=1, fmax=30, show=False) | ||
|
||
# Saving the figure so it can be accessed by the pdf creation. Automatically deleted when added to the pdf. | ||
plt.tight_layout() | ||
plt.savefig("power_spectrum.png") | ||
plt.show(block=False) | ||
plt.pause(10) | ||
plt.close() | ||
|
||
# Epoching | ||
# Create an array containing the timestamps and type of each stimulus (i.e. face or house) | ||
events = find_events(raw) | ||
|
||
# Create an MNE Epochs object representing all the epochs around stimulus presentation | ||
epochs = Epochs(raw, events=events, event_id=event_id, | ||
tmin=tmin, tmax=tmax, baseline=baseline, | ||
reject=reject, preload=preload, | ||
verbose=verbose, picks=picks) | ||
|
||
print('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100) | ||
print(len(epochs.events), 'events found') | ||
print(epochs) | ||
|
||
experimental_parameters = {"eeg_device": device_name, "experiment_name": experiment, "subject_id": subject, "session_nb": session, "example_flag": example, "drop_percent": (1 - len(epochs.events)/len(events)) * 100, "epochs_chosen": len(epochs.events)} | ||
|
||
return epochs, experimental_parameters | ||
|
||
|
||
def make_erp_plot(epochs, experimental_parameters:Dict, conditions=OrderedDict(House=[1],Face=[2]), ci=97.5, n_boot=1000, title='', | ||
diff_waveform=None, #(1, 2)) | ||
channel_order=[1,0,2,3]): | ||
""" | ||
Plots the ERP for the specified conditions. | ||
Parameters | ||
---------- | ||
epochs : MNE Epochs object | ||
conditions : OrderedDict holding the conditions to plot | ||
ci: confidence interval | ||
n_boot: number of bootstrap samples | ||
title: title of the plot | ||
diff_waveform: tuple of two integers indicating the channels to compare | ||
channel_order: list of integers indicating the order of the channels to plot | ||
""" | ||
|
||
fig, ax = plot_conditions(epochs, conditions=conditions, | ||
ci=97.5, n_boot=1000, title='', | ||
diff_waveform=None, #(1, 2)) | ||
channel_order=[1,0,2,3]) # reordering of epochs.ch_names according to [[0,2],[1,3]] of subplot axes | ||
|
||
# Autoscaling the y axis to a tight fit to the ERP | ||
for i in [0,1,2,3]: ax[i].autoscale(tight=True) | ||
|
||
# Saving the figure so it can be accessed by the pdf creation. Automatically deleted when added to the pdf. | ||
# Makes sure that the axis labels are not cut out | ||
plt.tight_layout() | ||
plt.savefig("erp_plot.png") | ||
plt.show(block=False) | ||
plt.pause(10) | ||
plt.close() | ||
|
||
# Creating the pdf, needs to be discussed whether we want to call it here or seperately. | ||
create_pdf(experimental_parameters) | ||
|
||
def create_pdf(experimental_parameters:Dict): | ||
"""Creates analysis report using the power spectrum and ERP plots that are saved in the directory""" | ||
|
||
# Unpack the experimental parameters | ||
eegdevice, experiment, subject, session, example, drop_percentage, epochs_chosen = experimental_parameters.values() | ||
|
||
# Getting the directory where the report should be saved | ||
save_dir = get_save_directory(experiment=experiment, eegdevice=eegdevice, subject=subject, session=session, example=example, label="analysis") | ||
|
||
#get whole filepath | ||
filepath = os.path.join(save_dir, 'analysis_report_{}.html'.format(datetime.now().strftime("%d-%m-%Y_%H-%M-%S"))) | ||
|
||
# Get the report | ||
report_html = get_html(experimental_parameters) | ||
|
||
# Save html file | ||
with open(filepath, 'w') as f: | ||
f.write(report_html) | ||
|
||
# Informing the user that the report has been saved | ||
print('Analysis report saved to {}\n'.format(filepath)) | ||
print("Open the report by clicking the following link: {}{}".format("file:///", filepath)) | ||
|
||
def get_save_directory(experiment, eegdevice, subject, session, example, label): | ||
""" Returns save directory as a String for the analysis report """ | ||
|
||
if not example: | ||
site='local' | ||
else: | ||
site='eegnb_examples' | ||
|
||
# Getting the directory where the analysis report should be saved | ||
save_path = os.path.join(os.path.expanduser("~/"),'.eegnb', label) | ||
save_path = os.path.join(save_path, experiment, site, eegdevice, "subject{}".format(subject), "session{}".format(session)) | ||
|
||
# Creating the directory if it doesn't exist | ||
if not os.path.isdir(save_path): | ||
os.makedirs(save_path) | ||
|
||
return save_path | ||
|
||
def create_analysis_report_(experiment, eegdevice, subject=None, session=None, data_path=None, bluemuse_file_fix=False): | ||
""" Interface with the erp plot function, basically cli type instructions """ | ||
|
||
# Prompt user to enter options and then take inputs and do the necessary | ||
epochs, experimental_parameters = load_eeg_data(experiment=experiment, subject=subject, session=session, device_name=eegdevice, example=False, fnames=data_path) | ||
make_erp_plot(epochs, experimental_parameters) | ||
|
||
def example_analysis_report(): | ||
""" Example of how to use the analysis report function """ | ||
|
||
experiment = ["visual-N170", "visual-P300"] | ||
experiment_choice = experiment[int(input("Choose an experiment: {} 0 or 1\n".format(experiment)))] | ||
|
||
if experiment_choice == "visual-N170": | ||
epochs, experimental_parameters = load_eeg_data(experiment_choice, example=True) | ||
make_erp_plot(epochs, experimental_parameters) | ||
else: | ||
epochs, experimental_parameters = load_eeg_data('visual-P300', device_name='muse2016', event_id={'Non-Target': 1, 'Target': 2}, example=True) | ||
make_erp_plot(epochs, experimental_parameters, conditions=OrderedDict(NonTarget=[1],Target=[2])) |
Oops, something went wrong.