Notebook

Loading of Miller ECoG data of faces/houses (+ noise)¶

includes some visualizations

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# @title Data retrieval
import os, requests

fname = 'faceshouses.npz'
url = "https://osf.io/argh7/download"

if not os.path.isfile(fname):
  try:
    r = requests.get(url)
  except requests.ConnectionError:
    print("!!! Failed to download data !!!")
  else:
    if r.status_code != requests.codes.ok:
      print("!!! Failed to download data !!!")
    else:
      with open(fname, "wb") as fid:
        fid.write(r.content)

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# @title Install packages (`nilearn`, `nimare`), import `matplotlib` and set defaults
# install packages to visualize brains and electrode locations
!pip install nilearn --quiet
!pip install nimare --quiet

from matplotlib import rcParams
from matplotlib import pyplot as plt
rcParams['figure.figsize'] = [20, 4]
rcParams['font.size'] = 15
rcParams['axes.spines.top'] = False
rcParams['axes.spines.right'] = False
rcParams['figure.autolayout'] = True

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# @title Data loading
import numpy as np

alldat = np.load(fname, allow_pickle=True)['dat']

# select just one of the recordings here.
dat1 = alldat[1][0]
dat2 = alldat[1][1]

print(dat1.keys())
print(dat2.keys())

dict_keys(['t_off', 'stim_id', 't_on', 'srate', 'V', 'scale_uv', 'locs', 'hemisphere', 'lobe', 'gyrus', 'Brodmann_Area'])
dict_keys(['stim_id', 'stim_cat', 'stim_noise', 't_on', 't_off', 'key_press', 'V', 'categories', 'scale_uv', 'locs', 'hemisphere', 'lobe', 'gyrus', 'Brodmann_Area'])

Dataset info¶

This is one of multiple ECoG datasets from Miller 2019, recorded in a clinical settings with a variety of tasks. We plan to curate a few more before NMA starts. Raw data here:

https://exhibits.stanford.edu/data/catalog/zk881ps0522

alldat contains 7 subjects each with two sessions dat1 and dat2, and was originally used in these publications:

Miller, Kai J., et al. "Face percept formation in human ventral temporal cortex." Journal of neurophysiology 118.5 (2017): 2614-2627.

Miller, Kai J., et al. "The physiology of perception in human temporal lobe is specialized for contextual novelty." Journal of neurophysiology 114.1 (2015): 256-263.

Miller, Kai J., et al. "Spontaneous decoding of the timing and content of human object perception from cortical surface recordings reveals complementary information in the event-related potential and broadband spectral change." PLoS computational biology 12.1 (2016): e1004660.

Miller, Kai J., et al. "The physiology of perception in human temporal lobe is specialized for contextual novelty." Journal of neurophysiology 114.1 (2015): 256-263.

In this task, subjects in a clinical settings (with ECoG implants) are passively shown faces and house during the first experiment (dat1). Then in the second experiment in the same subjects (dat2), noise is added to face and houses images and the subject has to detect the faces by pressing a key. Two of the subjects don't have keypresses.

Sample rate is always 1000Hz, and the ECoG data has been notch-filtered at 60, 120, 180, 240 and 250Hz, followed by z-scoring across time and conversion to float16 to minimize size.

Experiment 1:

dat1['V']: continuous voltage data (time by channels)
dat1['srate']: acquisition rate (1000 Hz). All stimulus times are in units of this.
dat1['t_on']: time of stimulus onset in data samples
dat1['t_off']: time of stimulus offset, always 400 samples after t_on
dat1['stim_id]: identity of stimulus from 1-100, with 1-50 being houses and 51-100 being faces
dat1['locs]: 3D electrode positions on the brain surface

Experiment 2:

dat2['V]: continuous voltage data (time by channels)
dat2['srate']: acquisition rate (1000 Hz). All stimulus times are in units of this.
dat2['t_on']: time of stimulus onset in data samples
dat2['t_off']: time of stimulus offset, always 1000 samples after t_on, with no inter-stimulus interval
dat2['stim_id]: identity of stimulus from 1-600 (not really useful, since we don't know which ones are the same house/face)
dat2['stim_cat']: stimulus category (1 = house, 2 = face)
dat2['stim_noise']: percent noise from 0 to 100
dat2['key_press']: when the subject thought the image was a face
dat2['categories']: categories legend (1 = house, 2 = face)
dat2['locs]: 3D electrode positions on the brain surface

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from nilearn import plotting
from nimare import utils

plt.figure(figsize=(8, 8))
locs = dat1['locs']
view = plotting.view_markers(utils.tal2mni(locs),
                             marker_labels=['%d'%k for k in np.arange(locs.shape[0])],
                             marker_color='purple',
                             marker_size=5)
view

ERROR:duecredit:Failed to import duecredit due to No module named 'duecredit'

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