Notebook

Loading of Miller ECoG data of the memory nback task¶

includes some visualizations

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# @title Data retrieval
import os, requests

fname = 'memory_nback.npz'
url = "https://osf.io/xfc7e/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[1][1]

print(dat.keys())

dict_keys(['V', 't_off', 'locs', 'srate', 'scale_uv', 't_on', 'target', 'stim_id', 'response', 'rt', 'expinfo', '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 3 sessions from 3 subjects, and was not yet used in a publication, so it's fresh data for you to analyze! The following references using other datasets may be useful for the memory n-back task:

Brouwer, Anne-Marie, et al. "Estimating workload using EEG spectral power and ERPs in the n-back task." Journal of Neural Engineering 9.4 (2012): 045008. doi: 10.1088/1741-2560/9/4/045008
Grissmann, Sebastian, et al. "Electroencephalography based analysis of working memory load and affective valence in an n-back task with emotional stimuli." Frontiers in Human Neuroscience 11 (2017): 616. doi: 10.3389/fnhum.2017.00616

Each subject's data is divided into 5 sessions: fixation, exp1, exp2, exp3, fixation, which are consecutive blocks in the data structure (i.e. alldat[0][0], alldat[0][1], ..., alldat[0][4] for subject 0). Exp1 is a "0-back" memory experiment, where the subjects have to identify the picture of one of the houses (stimid = 10), which they have memorized at the beginning of the stimulus block. Exp2 is a 1-back memory experiment, where the participants have to respond to images of repeated houses. Finally, exp3 is 2-back where participants respond to pictures of houses that are repeated after another intervening random picture. This task was hard for these participants and most of them did not really respond at all, but the data can still be used to ask questions about overall shifts of neural activity in a hard task when the subjects feel overwhelmed by the memory requirements.

Note that for the third/last subject, no response data was collected, so only the first two subjects can be analyzed for their correct / incorrect trials.

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 for each block within each subject:

dat['V']: continuous voltage data (time by channels)
dat['expinfo']: experiment type for this block
dat['srate']: sampling rate for voltage data, always 1000Hz
dat['t_on']: time of stimulus onset in data samples
dat['t_off']: time of stimulus offset, usually 600 samples after t_on
dat['stim_id]: identity of house stimulus from 1 to 40. Stimulus 10 was the target in the 0-back task.
dat['target']: 0 or 1, indicates if this stimulus requires a response
dat['response']: 0 or 1, indicates if the subject actually made a response
dat['rt']: reaction time for trials with a response in voltage data samples (1000Hz).
dat['locs']: 3D locations of the electrodes on the cortical surface

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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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