An Introduction To The Event Related Potential Technique

By M. Schmid. 2016

3,753 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9e5b770b38f90bb8a63ebab655450646d0ead81f

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Citations in the corpus

Investigating the efficacy of neurofeedback training for expediting expertise and excellence in sport (2015, unknown): “The null effects of pressure on performance could be attributed to the high number of trials that were required to generate meaningful EEG data (e.g., Luck, 2005).”
The steady-state visual evoked potential in vision research: A review. (2015, methodology): “This has been cited as a major limitation of the method (Luck, 2005).”
The feedback-related negativity (FRN) revisited: New insights into the localization, meaning and network organization (2014, background): “Most source localization studies depend on strong prior constraints, such as the number of possible dipoles (Luck, 2005).”
What do we mean by prediction in language comprehension? (2015, background): “Unlike evoked ERP or MEG responses, which index neural activity that is time-locked to specific events (Luck, 2014), and which are therefore best suited to detecting facilitation when a new incoming stimulus appears, low-frequency oscillatory activity may be better suited for capturing top-down…”
Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open‐source TESA software (2017, unknown): “Filtering is typically applied either during acquisition or as a first step in analysis to remove unwanted low frequency drifts (high-pass filtering) and high-frequency noise (low-pass filtering) from EEG signal (Luck, 2005).”
How to Get Statistically Significant Effects in Any ERP Experiment (and Why You Shouldn’t) (2017, methodology): “Mean amplitude is typically superior to peak amplitude in other ways, however, such as sensitivity to high-frequency noise (Clayson, Baldwin, & Larson, 2013; Luck, 2014).”
Heuristic and analytic processes in reasoning: an event-related potential study of belief bias. (2014, methodology): “We analyzed this using mean amplitude, which is not biased by number of trials, unlike peak amplitude, and so the differences on this measure are unlikely to be the result of an artifact (Luck, 2005).”
The Bilingual Adaptation: How Minds Accommodate Experience (2017, background): “Luck (2014) makes a similar point about interpreting differences in ERP waveforms: Electrophysiology can provide evidence of “covert” mental activity that provides a reliable measure of discrimination, processing, and interpretation.”
Using EEG to Predict Consumers’ Future Choices (2015, methodology): “…EEG also has a high sampling rate (approximately 1–2 ms; Luck 2005), which enables identification of very fast changes in the neural signal over short time scales (approximately 50 ms; Luck 2005) that may carry strong predictive information about consumer preferences and choice behavior.”
Digital filter design for electrophysiological data--a practical approach. (2015, unknown): “Attenuating the highfrequency components with the low-pass filter results in earlier signal onset and later offset, reduced peak amplitude, and artificial oscillations with a frequency near the cutoff frequency (see e.g., Luck, 2005 for a more detailed discussion of these oscillations).”
Autobiographically Significant Concepts: More Episodic than Semantic in Nature? An Electrophysiological Investigation of Overlapping Types of Memory (2015, methodology): “Before averaging, trials contaminated by excessive artifacts were rejected automatically with a step function (Luck, 2005) with a voltage threshold of ±100 μV in moving windows of 200 msec and with a window step of 100 msec.”
Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies (2018, background): “Event-related potentials (ERPs) are the average changes in the EEG signal in response to a stimulus, and characteristic ERP responses are referred to as components [131].”
Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research (2017, unknown): “Speciﬁcations to enhance the quality of data collection were speciﬁcally highlighted in the seminal Picton paper (Picton et al., 2000; see also Luck, 2014) in which issues such as electrode type, electrode quality (e.g., Coles et al., 1986; Kutas, 1997), the minimum number of electrodes necessary…”
The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli (2016, background): “Traditionally, research on the electrophysiology of sensory processing in humans has focused on the rather special case of brief, isolated stimuli because of the need to time-lock to discrete sensory events in order to estimate event-related potentials (ERPs; Handy, 2005; Luck, 2014).”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “Recently, several toolboxes have been developed that implement the methods described in the rest of this paper; the PyMVPA toolbox (Hanke, Halchenko, Sederberg, Hanson, et al., 2009; www.pymvpa.org) handles both fMRI and M/EEG data using the open-source Python language (Hanke, Halchenko, Sederberg, ”
ERPLAB: an open-source toolbox for the analysis of event-related potentials (2014, methodology): “…in the waveforms (Donchin and Hefﬂey, 1978). use of peaks to represent the magnitude and timing of the underlying ERP components persisted long after computers took the job of quantifying the data, even though the peaks often misrepresent the underlying components (see Chapter 2 in Luck, 2005).”
A technical guide to tDCS, and related non-invasive brain stimulation tools (2015, background): “There are no specific acquisition parameters in terms of sampling rate, impedance, or online filtering outside of the general recommendations for EEG recordings (Keil et al., 2014; Luck, 2014).”
Emotion matters: Implications for distracted driving (2015, background): “It is generally considered that the morphology, timing, and topography of ERP components reflect various ongoing cognitive processes, including those related to attention and working memory (Luck, 2005).”