Single-Trial ERP Component Analysis Using a Spatiotemporal LCMV Beamformer

Vliet, Marijn van; Chumerin, Nikolay; Deyne, Simon De; Wiersema, Jan R.; Fias, Wim; Storms, Gerrit; Hulle, Marc M. Van

doi:10.1109/tbme.2015.2468588

Cited by 41 publications

(45 citation statements)

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“…This difference (albeit in the short time-range only) could be attributed to the slight (statistically not significant) difference in forward association strength (FAS) between the two stimulus groups (note, that this difference was common to both experimental versions). The absence of inhibition in the long experimental version, where subjects were not aware of their L2 manipulation that could have demoted the processing of the homograph-related group, resulted in a difference between the homograph-related and control-related groups with a smaller N400 amplitude in response to homograph-related group (though for a short time only), which is also observed in other monolingual studies with semantic priming [65]. On the other hand, in the short experimental version, where subjects were aware about their L2 manipulation this difference was absent, probably due to an active inhibition of L2, which was also observed to influence the homograph-related group rendering its processing more difficult.…”

Section: Discussionsupporting

confidence: 62%

ERP Response Unveils Effect of Second Language Manipulation on First Language Processing

et al. 2016

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Lexical access in bilinguals has been considered either selective or non-selective and evidence exists in favor of both hypotheses. We conducted a linguistic experiment to assess whether a bilingual’s language mode influences the processing of first language information. We recorded event related potentials during a semantic priming paradigm with a covert manipulation of the second language (L2) using two types of stimulus presentations (short and long). We observed a significant facilitation of word pairs related in L2 in the short version reflected by a decrease in N400 amplitude in response to target words related to the English meaning of an inter-lingual homograph (homograph-unrelated group). This was absent in the long version, as the N400 amplitude for this group was similar to the one for the control-unrelated group. We also interviewed the participants whether they were aware of the importance of L2 in the experiment. We conclude that subjects participating in the long and short versions were in different language modes: closer to monolingual mode for the long and closer to bilingual mode for the short version; and that awareness about covert manipulation of L2 can influence the language mode, which in its turn influences the processing of the first language.

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Section: Discussionsupporting

confidence: 62%

ERP Response Unveils Effect of Second Language Manipulation on First Language Processing

et al. 2016

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“…How large these differences need to be in order for clusters to be differentiated depends on the SNR that can be achieved in estimating the amplitudes. In this study, we employed a spatiotemporal LCMV beamformer, which has been shown to produce more reliable estimates of the N400 amplitude than more traditional approaches, such as measuring the mean voltage in a fixed time window (van Vliet et al, 2016).…”

Section: Considerations Regarding the Interpretation Of The Resultsmentioning

confidence: 99%

“…The result of this summation represents the estimated amplitude of the N400 component of the ERP within that epoch. For an in-depth explanation and implementation details of the method, see van Vliet et al (2016).…”

Section: Beamformer Filtermentioning

confidence: 99%

“…The distribution of the slope ERP across the sensors at that time point was taken to be the spatial pattern for the N400 component (Figure 1, left). The temporal template was constructed by using the spatial template to create a spatial LCMV beamformer (van Vliet et al, 2016), the output of which represents an estimation of the summed activity at the cortical source locations of the N400 (Figure 1, right, gray line). This time course was further refined by multiplying it with a Gaussian kernel (μ = 400 msec, σ = 10 msec), which has the effect of limiting the nonzero values to a window of interest centered on the peak amplitude of the N400 (Figure 1, right, black line).…”

Section: Beamformer Filtermentioning

confidence: 99%

“…EEG was recorded while all pairwise combinations of the stimuli, a set of 14 written words, were presented sequentially to the participants. For the second word of each word pair (the target), the amplitude of the N400 component of the evoked EEG response was estimated using a linearly constrained minimum variance (LCMV) beamformer ( Van Veen, Van Drongelen, Yuchtman, & Suzuki, 1997), modified to be suitable for ERP analysis (Treder, Porbadnigk, Shahbazi Avarvand, Müller, & Blankertz, 2016;van Vliet et al, 2016;Wittevrongel & Van Hulle, 2016). This approach breaks down the problem of finding proper weights into two steps.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Organization of Semantic Memory through Unsupervised Analysis of Event-related Potentials

Vliet

Hulle

Salmelin

2018

Journal of Cognitive Neuroscience

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Single-trial ERP Quantification Using Neural Networks

et al. 2023

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Traditional approaches to quantify components in event-related potentials (ERPs) are based on averaging EEG responses. However, this method ignores the trial-to-trial variability in the component’s latency, resulting in a smeared version of the component and underestimates of its amplitude. Different techniques to quantify ERP components in single trials have therefore been described in literature. In this study, two approaches based on neural networks are proposed and their performance was compared with other techniques using simulated data and two experimental datasets. On the simulated dataset, the neural networks outperformed other techniques for most signal-to-noise ratios and resulted in better estimates of the topography and shape of the ERP component. In the first experimental dataset, the highest correlation values between the estimated latencies of the P300 component and the reaction times were obtained using the neural networks. In the second dataset, the single-trial latency estimation techniques showed an amplitude reduction of the N400 effect with age and ascertained this effect could not be attributed to differences in latency variability. These results illustrate the applicability and the added value of neural networks for the quantification of ERP components in individual trials. A limitation, however, is that simulated data is needed to train the neural networks, which can be difficult when the ERP components to be found are not known a priori. Nevertheless, the neural networks-based approaches offer more information on the variability of the timing of the component and result in better estimates of the shape and topography of ERP components.

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Single-Trial ERP Component Analysis Using a Spatiotemporal LCMV Beamformer

Cited by 41 publications

References 44 publications

ERP Response Unveils Effect of Second Language Manipulation on First Language Processing

ERP Response Unveils Effect of Second Language Manipulation on First Language Processing

Exploring the Organization of Semantic Memory through Unsupervised Analysis of Event-related Potentials

Single-trial ERP Quantification Using Neural Networks

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