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Review materials for Tressoldi et al 2014

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DataCite Commons2020-09-04 更新2024-07-27 收录
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Supplementary materials for my review of Tressoldi P et al. 2014. "Brain-to-Brain (mind-to-mind) interaction at distance: a confirmatory study" Version 2. StimulusTimecourses.png: Stimulus time courses extracted from raw data in Tressoldi P, Pederzoli L, Bilucaglia M et al. v2 "Brain-to-Brain (mind-to-mind) interaction at distance" 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 P, Pederzoli L, Bilucaglia M et al. v2 "Brain-to-Brain (mind-to-mind) interaction at distance". 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" to the remaining "testing samples". 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.
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figshare
创建时间:
2016-01-19
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