figshare
Browse
1/1
2 files

Review materials for Tressoldi et al 2014, version 2

Version 5 2014-09-30, 08:48
Version 4 2014-09-30, 08:48
Version 3 2014-09-30, 08:39
Version 2 2014-09-30, 08:20
Version 1 2014-09-29, 19:03
dataset
posted on 2014-09-30, 08:48 authored by D. Sam SchwarzkopfD. Sam Schwarzkopf

Supplementary materials for my review of Tressoldi  et al. 2014. "Brain-to-Brain (mind-to-mind) interaction at distance: a confirmatory study" version 2.

 

StimulusTimecourses.png:

Stimulus time courses were extracted from coincidences data files. Each row corresponds to the stimulus time course for one of the 20 pairs of participants. Upwards bumps in these plots indicate stimulus (signal) periods, the rest are baseline (silence) periods. The strong correlation between the different stimulus protocols is immediately obvious. For the majority (13) of pairs the first stimulus occurred after one minute. For the remainder (7) the first stimulus occurred after 2 minutes. In addition to the high predictability of these time courses, this plot also demonstrates that the intial silence period was not randomized to be 1, 2, or 3 minutes as claimed in the manuscript, but simply either 1 or 2 minutes.

 

TemporalCorrelations.png:

Temporal correlations between raw data randomly sampled into 50% training and 50% testing samples as done in the analysis by Tressoldi et al.

Each panel shows a 2D density histogram. Hotter colours indicate a greater frequency of data points clustering in that location. These plots compare the randomly assigned "training samples" (x-axis) to the remaining "testing samples" (y-axis). The four columns correspond to EEG channels 1-4. The four rows are four repetitions in which a new set of training/test samples have been assigned randomly.

For most plots the strong correlation between the randomly assigned samples is evident even on visual inspection. Even for the plots that seem to show less of a correlation (e.g. the fourth column) the Pearson's correlation is extremely significant. It should not be surprising that a powerful classifier can exploit such correlations to decode arbitrary stimulus labels provided the stimulus labels aren't too high frequency.

History

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC