Brain event-related potentials predict individual differences in inhibitory control

Rueda‐Delgado, Laura M.; O’Halloran, Laura; Enz, Nadja; Ruddy, Kathy; Kiiski, Hanni; Bennett, Marc; Farina, Francesca R; Jollans, Lee; Vahey, Nigel; Whelan, Robert

doi:10.1016/j.ijpsycho.2019.03.013

Cited by 20 publications

(11 citation statements)

References 53 publications

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“…Future research should also consider using alternative methods such as techniques involving machine learning methods (Pereira et al, 2009). The utilization of machine learning methods in ERP research is still rare (Rueda-Delgado et al, 2019), but it could provide valuable information in future studies tackling the relationship between neural activity and CB (e.g., Hakim et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Can Brain Waves Really Tell If a Product Will Be Purchased? Inferring Consumer Preferences From Single-Item Brain Potentials

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et al. 2019

Front. Integr. Neurosci.

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Recent research has shown that event-related brain potentials (ERPs) recorded while participants view lists of different consumer goods can be modulated by their preferences toward these products. However, it remains largely unknown whether ERP activity specific to a single consumer item can be informative about whether or not this item will be preferred in a shopping context. In this study, we examined whether single-item ERPs could reliably predict consumer preferences toward specific consumer goods. We recorded scalp EEG from 40 participants while they were viewing pictures of consumer goods and we subsequently asked them to indicate their preferences for each of these items. Replicating previous results, we found that ERP activity averaged over the six most preferred products was significantly differentiated from ERP activity averaged across the six least preferred products for three ERP components: The N200, the late positive potential (LPP) and positive slow waves (PSW). We also found that using single-item ERPs to infer behavioral preferences about specific consumer goods led to an overall predictive accuracy of 71%, although this figure varied according to which ERPs were targeted. Later positivities such as the LPP and PSW yielded relatively higher predictive accuracy rates than the frontal N200. Our results suggest that ERPs related to single consumer items can be relatively accurate predictors of behavioral preferences depending on which type of ERP effects are chosen by the researcher, and ultimately on the level of prediction errors that users choose to tolerate.

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Section: Discussionmentioning

confidence: 99%

Can Brain Waves Really Tell If a Product Will Be Purchased? Inferring Consumer Preferences From Single-Item Brain Potentials

Goto

Lim

Shee

et al. 2019

Front. Integr. Neurosci.

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“…This process was repeated for each participant. Further, internal validation sets (inner cross-validation) were performed to choose the hyperparameters of the model 64 . Thus, the same hyperparameters were selected in the external validation sets 65 .…”

Section: Methodsmentioning

confidence: 99%

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning

et al. 2022

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Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness.

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“…Participants provided written informed consent. The raw data from this dataset formed the basis of another study that did not focus on spectral properties (Rueda-Delgado et al, 2019). The inclusion and exclusion criteria of each project are available in the Supplementary Material in Rueda-Delgado et al (2019).…”

Section: Participantsmentioning

confidence: 99%

“…were calculated and datasets with z-scores > 3 were excluded from further analysis. Fifteen participants were rejected from further analysis due to these artefacts or noisy data (in accordance with the exclusion criteria in Rueda-Delgado et al, 2019). The remaining 220 participants were subjected to further analysis.…”

Section: Eeg Recording and Preprocessingmentioning

confidence: 99%

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

et al. 2021

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In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesised that the right inferior frontal cortex plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalographyderived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed prior to successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) electroencephalography Stop Signal Task dataset (n=218) to examine averaged normalised β-power, β-burst rate and β-burst 'volume' (which we defined as burst duration x frequency span x amplitude). We first sought to optimise the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful vs. failed stopping and to (2) predict individual Stop Signal Reaction Time. β-Burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalised β-power. The classification model generalised to an external dataset (n=201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition. 3 Significance StatementThe electroencephalography-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of βactivity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activityacross the whole scalp and with high temporal precisionwe employed machine learning on two large datasets. Spatial and temporal features of β-burst 'volume' (duration x frequency span x amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalised β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

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Brain event-related potentials predict individual differences in inhibitory control

Cited by 20 publications

References 53 publications

Can Brain Waves Really Tell If a Product Will Be Purchased? Inferring Consumer Preferences From Single-Item Brain Potentials

Can Brain Waves Really Tell If a Product Will Be Purchased? Inferring Consumer Preferences From Single-Item Brain Potentials

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

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