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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
License: CC BY
Data sources: ZENODO
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Dataset: Brain Negativity As An Indicator Of Predictive Error Processing: The Contribution Of Visual Action Effect Monitoring

Authors: Joch, M., Hegele, M., Maurer, H., Müller, H. & Maurer, L.K.;

Dataset: Brain Negativity As An Indicator Of Predictive Error Processing: The Contribution Of Visual Action Effect Monitoring

Abstract

There are two files for each subject: 1. sub##_error.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below). 2. sub##_hit.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 7 cm) in the task (segment and electrode information can be found below). The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodes n represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726 (850 ms after ball release) in every segment. datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized, i.e. hit/error #1 -> row 1 to 1400, hit/error #2 -> row 1401 to 2800, ..., hit/error #n -> (n-1) * 1400 + 1 to n * 1400 electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]

This research was supported by the Deutsche Forschungsgemeinschaft funded Collaborative Research Center on "Cardinal Mechanisms of Perception" Grant SFB-TRR 135.

{"references": ["Joch et al. (2017) doi: 10.1152/jn.00036.2017"]}

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Keywords

forward model, action effect monitoring, EEG, error negativity, ERP

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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