EEG variability: Task-driven or subject-driven signal of interest?

Gibson, Erin; Lobaugh, Nancy J.; Joordens, Steve; McIntosh, Anthony R.

doi:10.1016/j.neuroimage.2022.119034

Cited by 17 publications

(22 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From previous work, the assumption when building a model is that the relationship between the neural response and the stimulus feature will stabilize over time (Holdgraf et al, 2017). However, such an assumption may not always be the case because of varying signal-to-noise ratios for certain subjects or even factors such as participant attention or adaptation to the stimuli (Gibson et al, 2022). Calculating the stability of the model performance by observing changes in the weights can provide a metric The intercept represents the effect of the combined model, whereas the relative effects of the envelope, phonological features, and pitch are shown.…”

Section: How Do Receptive Fields Stabilize Over Time?mentioning

confidence: 99%

Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG

Desai

Field²,

Hamilton³

2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

In many experiments that investigate auditory and speech processing in the brain using electroencephalography (EEG), the experimental paradigm is often lengthy and tedious. Typically, the experimenter errs on the side of including more data, more trials, and therefore conducting a longer task to ensure that the data are robust and effects are measurable. Recent studies used naturalistic stimuli to investigate the brain's response to individual or a combination of multiple speech features using system identification techniques, such as multivariate temporal receptive field (mTRF) analyses. The neural data collected from such experiments must be divided into a training set and a test set to fit and validate the mTRF weights. While a good strategy is clearly to collect as much data as is feasible, it is unclear how much data are needed to achieve stable results. Furthermore, it is unclear whether the specific stimulus used for mTRF fitting and the choice of feature representation affects how much data would be required for robust and generalizable results. Here, we used previously collected EEG data from our lab using sentence stimuli and movie stimuli as well as EEG data from an open-source dataset using audiobook stimuli to better understand how much data needs to be collected for naturalistic speech experiments measuring acoustic and phonetic tuning. We found that the EEG receptive field structure tested here stabilizes after collecting a training dataset of approximately 200 s of TIMIT sentences, around 600 s of movie trailers training set data, and approximately 460 s of audiobook training set data. Thus, we provide suggestions on the minimum amount of data that would be necessary for fitting mTRFs from naturalistic listening data. Our findings are motivated by highly practical concerns when working with children, patient populations, or others who may not tolerate long study sessions. These findings will aid future researchers who wish to study naturalistic speech processing in healthy and clinical populations while minimizing participant fatigue and retaining signal quality.

show abstract

Section: How Do Receptive Fields Stabilize Over Time?mentioning

confidence: 99%

Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG

Desai

Field²,

Hamilton³

2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…individual EEG variability was stable across multiple tasks, which further indicates that EEG variability is a subject-driven, inner-state measurement. 14 Overall, our work provides evidence that the individual differences in social conformity are stably associated with their individual neural variability, which might be a stable inner-state measurement and related to mindset stability.…”

Section: F I G U R Ementioning

confidence: 56%

“…In light of the potential relation between individual differences in social conformity and personal mindset stability, we asked how to use neural signals to indicate mindset stability. Recently, tracking moment-to-moment variability in neural activity has emerged; [13][14][15][16] larger neural variability may reflect a greater dynamic range, which is beneficial to adaptability and permits a greater range of responses to a greater range of stimuli. 15,17 Measures of brain signal variability demonstrate highly predictive results for brain functioning.…”

Section: F I G U R Ementioning

confidence: 99%

“…17,[20][21][22] While most of the neural variability is calculated within single trials or blocks, some studies have calculated the across-trial neural variability, which is more interpretable and has been shown to relate to various functional roles, such as human perception [23][24][25][26] and cognitive skill learning tasks. 14 Previous research also suggests EEG variability as a subject-driven measure of individual differences. 14 However, to our knowledge, no study has investigated the functional role of neural variability in social behavior.…”

Section: F I G U R Ementioning

confidence: 99%

“…14 Previous research also suggests EEG variability as a subject-driven measure of individual differences. 14 However, to our knowledge, no study has investigated the functional role of neural variability in social behavior. 16 Neural signatures (e.g., electroencephalograms [EEGs]) from the frontal and occipital visual areas may be associated with the effect of social influence on face perception.…”

Section: F I G U R Ementioning

confidence: 99%

See 2 more Smart Citations

Social conformity is associated with inter‐trial electroencephalogram variability

Zhang

et al. 2023

Annals of the New York Academy of Sciences

View full text Add to dashboard Cite

Human society encompasses diverse social influences, and people experience events differently and may behave differently under such influence, including in forming an impression of others. However, little is known about the underlying neural relevance of individual differences in following others’ opinions or social norms. In the present study, we designed a series of tasks centered on social influence to investigate the underlying relevance between an individual's degree of social conformity and their neural variability. We found that individual differences under the social influence are associated with the amount of inter‐trial electroencephalogram (EEG) variability over multiple stages in a conformity task (making face judgments and receiving social influence). This association was robust in the alpha band over the frontal and occipital electrodes for negative social influence. We also found that inter‐trial EEG variability is a very stable, participant‐driven internal state measurement and could be interpreted as mindset instability. Overall, these findings support the hypothesis that higher inter‐trial EEG variability may be related to higher mindset instability, which makes participants more vulnerable to exposed external social influence. The present study provides a novel approach that considers the stability of one's endogenous neural signal during tasks and links it to human social behaviors.

show abstract

Cognitive investigation on the effect of augmented reality-based reading on emotion classification performance: A new dataset

Daşdemir

2022

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

EEG variability: Task-driven or subject-driven signal of interest?

Cited by 17 publications

References 34 publications

Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG

Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG

Social conformity is associated with inter‐trial electroencephalogram variability

Cognitive investigation on the effect of augmented reality-based reading on emotion classification performance: A new dataset

Contact Info

Product

Resources

About