Abstract
Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of steady-state evoked responses (i.e., oscillations in the electroencephalogram phase-locked to the driving stimuli). The classic interpretation of the amplitude of the steady-state evoked responses assumes a stereotypical time-invariant neural response plus random background fluctuations, such that averaging over repeated presentations of the stimulus recovers the stereotypical response. This approach ignores the dynamics of the steady-state, as in the case of the adaptation elicited by prolonged exposures to the stimulus. To analyze the dynamics of steady-state responses, it can be assumed that the time evolution of the response amplitude is the same in different stimulation runs separated by sufficiently long breaks. Based on this assumption, a method to characterize the time evolution of steady-state responses is presented. A sufficiently large number of recordings are acquired in response to the same experimental condition. Experimental runs (recordings) are column-wise averaged (i.e., runs are averaged but epoch within recordings are not averaged with the preceding segments). The column-wise averaging allows analysis of steady-state responses in recordings with remarkably high signal-to-noise ratios. Therefore, the averaged signal provides an accurate representation of the time evolution of the steady-state response, which can be analyzed in both the time and frequency domains. In this study, a detailed description of the method is provided, using steady-state visually evoked potentials as an example of a response. Advantages and caveats are evaluated based on a comparison with single-trial methods designed to analyze neural entrainment.
Original language | English |
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Article number | e59898 |
Journal | Journal of Visualized Experiments |
Volume | 2019 |
Issue number | 147 |
DOIs | |
State | Published - 2019 |
Externally published | Yes |
Bibliographical note
Funding Information:The authors gratefully acknowledge Lucía Zepeda, Grace A. Whitaker, and Nicolas Nieto for their contributions to video production. This work was supported in part by CONICYT programs BASAL FB0008, MEC 80170124 and PhD scholarship 21171741, as well as the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health under award number P50DC015446. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© 2019 Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
ASJC Scopus subject areas
- General Neuroscience
- General Chemical Engineering
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology