Notebook

Loading of Miller ECoG data of the joystick track task¶

includes some visualizations

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#@title Data retrieval
import os, requests

fname = 'joystick_track.npz'
url = "https://osf.io/6jncm/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', 'targetX', 'targetY', 'cursorX', 'cursorY', '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

dat contain 4 sessions from 4 subjects, and was used in these papers:

Schalk, G., et al. "Decoding two-dimensional movement trajectories using electrocorticographic signals in humans." Journal of Neural Engineering 4.3 (2007): 264. doi: 10.1088/1741-2560/4/3/012
Schalk, Gerwin, et al. "Two-dimensional movement control using electrocorticographic signals in humans." Journal of Neural Engineering 5.1 (2008): 75. doi: 10.1088/1741-2560/5/1/008

From the dataset readme:

During the study, each patient was in a semi-recumbent position in a hospital bed about 1 m from a computer monitor. The patient used a joystick to maneuver a white cursor track a green target moving counter-clockwise in a circle of diameter 85% of monitor height ~1m away. The hand used to control the joystick was contralateral to the implanted electrode array.

We also know that subject 0 was implanted in the left temporal lobe, while subject 2 was implanted in the right frontal lobe.

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.

Variables are:

dat['V']: continuous voltage data (time by channels)
dat['targetX']: position of the target on the screen
dat['targetY']: position of the target on the screen
dat['cursorX']: X position of the cursor controlled by the joystick
dat['cursorY']: X position of the cursor controlled by the joystick
dat['locs]: three-dimensional coordinates of the electrodes

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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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