Notebook

Loading of Miller ECoG data of the finger flexing task¶

includes some visualizations

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# @title Data retrieval
import os, requests

fname = 'fingerflex.npz'
url = "https://osf.io/5m47z/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`, `duecredit`), import `matplotlib` and set defaults
# install packages to visualize brains and electrode locations
!pip install nilearn --quiet
!pip install nimare --quiet
!pip install duecredit --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. This is subject 1, block 1.
dat = alldat[0][0]

print(dat.keys())

dict_keys(['V', 'dg', 't_on', 't_off', 'stim_id', 'stimtext', 'srate', 'locs', 'hemisphere', 'lobe', 'gyrus', 'Brodmann_Area'])

Dataset info¶

This is one of multiple ECoG datasets from Miller 2019, recorded in clinical settings with a variety of tasks. Raw data here:

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

This particular dataset has been used in the following publications:

Miller, Kai J., et al. "Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans." Journal of Neuroscience 29.10 (2009): 3132-3137.

Miller, Kai J., et al. "Human motor cortical activity is selectively phase-entrained on underlying rhythms." (2012): e1002655.

alldat contain 3 sessions from 3 subjects, and was used in this paper:

https://pubmed.ncbi.nlm.nih.gov/17873429/

From the dataset readme:

finger movement task, subjects were cued with a word displayed on a bedside monitor indicating which finger to move during 2- second movement trials. The subject performed self-paced movements in response to each of these cues, and they typically moved each finger 2–5 times during each trial, but some trials included many more movements. "stim" (time x 1): 0 – Inter-stimulus interval 1 – thumb 2 – index finger 3 – middle finger 4 – ring finger 5 – little finger

The motion of the fingers was captured with a dataglove, which was sampled at the same rate (1000Hz) as the voltage data. From the dataset readme:

finger flexions, measured from the hand contralateral to grid placement. Note that they are in the raw form input. They are in 40ms blocks, and have non-zero offset. The 5 columns correspond to the digits thumb-index-middle-ring-little in order. Finger positions were recorded using a 5 degree-of-freedom dataglove sensor (5 dt, Irvine, CA).

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 the entire recording and conversion to float16 to minimize size (please convert back to float32 for analysis).

Variables are:

dat['V']: continuous voltage data (time by channels)
dat['locs]: three-dimensional coordinates of the electrodes (n_channels by 3)
dat['dg']: digit flexions (time x number of fingers). This was preprocessed from the original data to remove a moving baseline.
dat['srate']: sampling rate (always 1000)
dat['t_on']: time of cue onset
dat['t_off']: time of cue offset (usually 2000ms after t_on)
dat['stim_id']: cue identity (which finger to flex)
dat['stimtext']: cue text on the screen

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from nilearn import plotting
from nimare import utils

plt.figure(figsize=(8, 8))
locs = dat['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

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