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Each trial was presented for 10 or The data were analyzed offline, using PowerDiva Host 4. Raw data were subjected to an off-line sample-by-sample thresholding procedure in which noisy and dead sensors were replaced by the average of the six nearest spatial neighbors. We then re-referenced the EEG data to the common average of all electrodes. For frequency domain analysis, the complex-value of Fourier coefficients were then averaged across all epochs per repetition, per condition separately for each participant.
For the time domain analysis, we computed the amplitude, cosine and sine values per condition and trial separately for each participant.
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The averages used for plotting were computed as vector averages of real and imaginary values of all participants. The filtered signals were then inverse-transformed into the time domain and RCA was run on this inverse transformed signals in time domain. After running RCA, we tested for pair-wise waveform differences between pattern types PMM — P2; PMM — feature-matched; P2 — feature-matched and ran a permutation testing procedure to correct for multiple comparisons, based on methods devised by Blair and Karniski see 52 and described in detail by Appelbaum and colleagues see Briefly, this procedure tested the null hypothesis that no differences were present between pattern types, by generating artificial data sets where the two pattern type labels were permuted randomly across participants.
Because each permutation sample contributes only its longest significant sequence to the reference distribution, this procedure implicitly compensates for the problem of multiple comparisons and is a valid test for the omnibus hypothesis of no difference between the waveforms at any time point. Furthermore, this test not only detected significant departures from the null hypothesis, but also localized the time periods when such departures occurred.
By applying both statistical approaches, we were better able to identify time periods when the responses depart from the null hypothesis. Grill-Spector, K. The lateral occipital complex and its role in object recognition. Vision Res. A model for global symmetry detection in dense images. Machilsen, B. The role of vertical mirror symmetry in visual shape detection. Cohen, E.
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Cortex 51 , 46—55 Cattaneo, Z. The role of the human extrastriate visual cortex in mirror symmetry discrimination: A TMS-adaptation study. Brain Cogn. Kohler, P. Dakin, S.
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Magnetoencephalographic signatures of visual form and motion binding. Brain Res. Alp, N. Frequency tagging yields an objective neural signature of Gestalt formation. EEG frequency tagging dissociates between neural processing of motion synchrony and human quality of multiple point-light dancers. Boremanse, A. An objective signature for visual binding of face parts in the human brain. Dissociation of part-based and integrated neural responses to faces by means of electroencephalographic frequency tagging. Fuchs, S. Attentional bias of competitive interactions in neuronal networks of early visual processing in the human brain.
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