Note
Go to the end to download the full example code. or to run this example in your browser via Binder
Decoding of a dataset after GLM fit for signal extraction¶
Full step-by-step example of fitting a GLM to perform a decoding experiment. In this decoding analysis, we will be doing a one-vs-all classification. We use the data from one subject of the Haxby dataset.
More specifically:
Download the Haxby dataset.
Extract the information to generate a glm representing the blocks of stimuli.
Analyze the decoding performance using a classifier.
Fetch example Haxby dataset¶
We download the Haxby dataset This is a study of visual object category representation
# By default 2nd subject will be fetched
import numpy as np
import pandas as pd
from nilearn.datasets import fetch_haxby
haxby_dataset = fetch_haxby()
# repetition has to be known
t_r = 2.5
[fetch_haxby] Dataset found in /home/runner/nilearn_data/haxby2001
Load the behavioral data¶
# Load target information as string and give a numerical identifier to each
behavioral = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
conditions = behavioral["labels"].to_numpy()
# Record these as an array of runs
runs = behavioral["chunks"].to_numpy()
unique_runs = behavioral["chunks"].unique()
# fMRI data: a unique file for each run
func_filename = haxby_dataset.func[0]
Build a proper event structure for each run¶
events = {}
# events will take the form of a dictionary of Dataframes, one per run
for run in unique_runs:
# get the condition label per run
conditions_run = conditions[runs == run]
# get the number of scans per run, then the corresponding
# vector of frame times
n_scans = len(conditions_run)
frame_times = t_r * np.arange(n_scans)
# each event last the full TR
duration = t_r * np.ones(n_scans)
# Define the events object
events_ = pd.DataFrame(
{
"onset": frame_times,
"trial_type": conditions_run,
"duration": duration,
}
)
# remove the rest condition and insert into the dictionary
events[run] = events_[events_.trial_type != "rest"]
Instantiate and run FirstLevelModel¶
We generate a list of z-maps together with their run and condition index
z_maps = []
conditions_label = []
run_label = []
# Instantiate the glm
from nilearn.glm.first_level import FirstLevelModel
glm = FirstLevelModel(
t_r=t_r,
mask_img=haxby_dataset.mask,
high_pass=0.008,
smoothing_fwhm=4,
memory="nilearn_cache",
memory_level=1,
verbose=1,
)
Run the GLM on data from each run¶
events[run].trial_type.unique()
from nilearn.image import index_img
for run in unique_runs:
# grab the fmri data for that particular run
fmri_run = index_img(func_filename, runs == run)
# fit the GLM
glm.fit(fmri_run, events=events[run])
# set up contrasts: one per condition
conditions = events[run].trial_type.unique()
for condition_ in conditions:
z_maps.append(glm.compute_contrast(condition_))
conditions_label.append(condition_)
run_label.append(run)
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee5365b0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee5365b0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee5e2b20>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee5e2b20>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca4f6070>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca4f6070>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1a85df0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1a85df0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1ebffd0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1ebffd0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca5f3040>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca5f3040>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca5dee50>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca5dee50>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee506d30>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee506d30>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f18d9c70>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f18d9c70>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1ab4760>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1ab4760>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca4437f0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca4437f0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91c972d400>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91c972d400>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
Generating a report¶
Since we have already computed the FirstLevelModel and have the contrast, we can quickly create a summary report.
from nilearn.image import mean_img
mean_img_ = mean_img(func_filename, copy_header=True)
report = glm.generate_report(
contrasts=conditions,
bg_img=mean_img_,
)
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Generating contrast-level figures...
[FirstLevelModel.generate_report] Generating design matrices figures...
[FirstLevelModel.generate_report] Generating contrast matrices figures...
This report can be viewed in a notebook.
In a jupyter notebook, the report will be automatically inserted, as above.
# We can access the report via a browser:
# report.open_in_browser()
# Or we can save as an html file.
from pathlib import Path
output_dir = Path.cwd() / "results" / "plot_haxby_glm_decoding"
output_dir.mkdir(exist_ok=True, parents=True)
report.save_as_html(output_dir / "report.html")
Build the decoding pipeline¶
To define the decoding pipeline we use Decoder object, we choose :
a prediction model, here a Support Vector Classifier, with a linear kernel
the mask to use, here a ventral temporal ROI in the visual cortex
although it usually helps to decode better, z-maps time series don’t need to be rescaled to a 0 mean, variance of 1 so we use standardize=False.
we use univariate feature selection to reduce the dimension of the problem keeping only 5% of voxels which are most informative.
a cross-validation scheme, here we use LeaveOneGroupOut cross-validation on the runs which corresponds to a leave-one-run-out
We fit directly this pipeline on the Niimgs outputs of the GLM, with corresponding conditions labels and run labels (for the cross validation).
from sklearn.model_selection import LeaveOneGroupOut
from nilearn.decoding import Decoder
decoder = Decoder(
estimator="svc",
mask=haxby_dataset.mask,
standardize=False,
screening_percentile=5,
cv=LeaveOneGroupOut(),
verbose=1,
)
decoder.fit(z_maps, conditions_label, groups=run_label)
# Return the corresponding mean prediction accuracy compared to chance
# for classifying one-vs-all items.
classification_accuracy = np.mean(list(decoder.cv_scores_.values()))
chance_level = 1.0 / len(np.unique(conditions))
print(
f"Classification accuracy: {classification_accuracy:.4f} / "
f"Chance level: {chance_level}"
)
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Resamping mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91c39a6eb0>
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.82724e+06mm^3
[Decoder.fit] Original screening-percentile: 5
[Decoder.fit] Corrected screening-percentile: 4.65085
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 96 out of 96 | elapsed: 7.4s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Classification accuracy: 0.7589 / Chance level: 0.125
Total running time of the script: (2 minutes 16.507 seconds)
Estimated memory usage: 1015 MB