Reduced premovement positivity during the stimulus-response interval precedes errors: Using single-trial and regression ERPs to understand performance deficits in ADHD

Neural correlates of confidence during decision formation in a perceptual judgment task

When we make a decision, we also estimate the probability that our choice is correct or accurate. This probability estimate is termed our degree of decision confidence. Recent work has reported event-related potential (ERP) correlates of confidence both during decision formation (the centro-parietal positivity component; CPP) and after a decision has been made (the error positivity component; Pe). However, there are several measurement confounds that complicate the interpretation of these findings. More recent studies that overcome these issues have so far produced conflicting results. To better characterise the ERP correlates of confidence we presented participants with a comparative brightness judgment task while recording electroencephalography. Participants judged which of two flickering squares (varying in luminance over time) was brighter on average. Participants then gave confidence ratings ranging from "surely incorrect" to "surely correct". To elicit a range of confidence ratings we manipulated both the mean luminance difference between the brighter and darker squares (relative evidence) and the overall luminance of both squares (absolute evidence). We found larger CPP amplitudes in trials with higher confidence ratings. This association was not simply a by-product of differences in relative evidence (which covaries with confidence) across trials. We did not identify postdecisional ERP correlates of confidence, except when they were artificially produced by pre-response ERP baselines. These results provide further evidence for neural correlates of processes that inform confidence judgments during decision formation.

Study of the human brain potentials variability effects in P300 based brain–computer interface

The P300-based brain–computer interfaces (P300 BCI) allow the user to select commands by focusing on them. The technology involves electroencephalographic (EEG) representation of the event-related potentials (ERP) that arise in response to repetitive external stimulation. Conventional procedures for ERP extraction and analysis imply that identical stimuli produce identical responses. However, the floating onset of EEG reactions is a known neurophysiological phenomenon. A failure to account for this source of variability may considerably skew the output and undermine the overall accuracy of the interface. This study aimed to analyze the effects of ERP variability in EEG reactions in order to minimize their influence on P300 BCI command classification accuracy. Healthy subjects aged 21–22 years (n = 12) were presented with a modified P300 BCI matrix moving with specified parameters within the working area. The results strongly support the inherent significance of ERP variability in P300 BCI environments. The correction of peak latencies in single EEG reactions provided a 1.5–2 fold increase in ERP amplitude with a concomitant enhancement of classification accuracy (from 71–78% to 92–95%, p < 0.0005). These effects were particularly pronounced in attention-demanding tasks with the highest matrix velocities. The findings underscore the importance of accounting for ERP variability in advanced BCI systems.

Interplay of self-other distinction and cognitive control mechanisms in a social automatic imitation task: An ERP study

The regulation of motor resonance processes in daily life is indispensable. The automatic imitation task is an experimental model of those daily-life motor resonance processes. Recent research suggests that both self-other distinction and cognitive control processes may be involved in interference control during automatic imitation. Yet, we lack a clear understanding of the chronological sequence of interacting processes. To this end, this study used event-related potentials (ERPs) to investigate the time course underlying interference control during automatic imitation. We moreover aimed to extend previous results by investigating its modulation by social context. Cognitive conflict/action monitoring was assessed with the N2, in an exploratory manner the N450, and the CRN components. The Pre-Motor Positivity (PMP), associated with movement initiation, was suggested as a possible correlate of the successful resolution of self-other distinction. The cognitive control/action monitoring ERP components were influenced by the social context manipulation and partly by congruency, while PMP amplitudes were only sensitive to congruency. In addition, the exploratorily investigated N450 component predicted response times on incongruent relative to congruent trials in the different social contexts. This suggested that cognitive control/action monitoring processes, reflected in the N450, are guiding behavioral outcomes. Overall, interference control may primarily be guided by processes of cognitive control/action monitoring, whilst being modulated by social context demands.

Neural generators involved in visual cue processing in children with attention-deficit/hyperactivity disorder (ADHD)

Event-related potentials (ERP) studies report alterations in the ongoing visuo-attentional processes in children with attention-deficit/hyperactivity disorder (ADHD). We hypothesized that the neural generators progressively recruited after a cue stimulus imply executive-related areas well before engagement in executive processing in children with ADHD compared to typically developed children (TDC). We computed source localization (swLORETA) of the ERP and ERSP evoked by the Cue stimulus during a visual Cue-Go/Nogo paradigm in 15 ADHD compared to 16 TDC. A significant difference in N200/P200 amplitude over the right centro-frontal regions was observed between ADHD and TDC, supported by a stronger contribution of the left visuo-motor coordination area, premotor cortex, and prefrontal cortex in ADHD. In addition, we recorded a greater beta power spectrum in ADHD during the 80-230 ms interval, which was explained by increased activity in occipito-parieto-central areas and lower activity in the left supramarginal gyrus and prefrontal areas in ADHD. Successive analysis of the ERP generators (0-500 ms with successive periods of 50 ms) revealed significant differences beginning at 50 ms, with higher activity in the ventral anterior cingulate cortex, premotor cortex, and fusiform gyrus, and ending at 400-500 ms with higher activity of the dorsolateral prefrontal cortex and lower activity of the posterior cingulate cortex in ADHD compared to TDC. The areas contributing to ERP in ADHD and TDC differ from the early steps of visuo-attentional processing and reveal an overinvestment of the executive networks interfering with the activity of the dorsal attention network in children with ADHD.

Driving Accidents, Driving Violations, Symptoms of Attention-Deficit-Hyperactivity (ADHD) and Attentional Network Tasks

Background: Iran has serious problems with traffic-related injuries and death. A major reason for traffic accidents is cognitive failure due to deficits in attention. In this study, we investigated the associations between traffic violations, traffic accidents, symptoms of attention-deficit/hyperactivity disorder (ADHD), age, and on an attentional network task in a sample of Iranian adults. Methods: A total of 274 participants (mean age: 31.37 years; 80.7% males) completed questionnaires covering demographic information, driving violations, traffic accidents, and symptoms of ADHD. In addition, they underwent an objective attentional network task (ANT), based on Posner’s concept of attentional networks. Results: More frequent traffic violations, correlated with lower age and poorer performance on the attentional network tasks. Higher symptoms of ADHD were associated with more accidents and more traffic violations, but not with the performance of the attentional tasks. Higher ADHD scores, a poorer performance on attentional network tasks, and younger age predicted traffic violations. Only higher symptoms of ADHD predicted more traffic accidents. Conclusions: In a sample of Iranian drivers, self-rated symptoms of ADHD appeared to be associated with traffic violations and accidents, while symptoms of ADHD were unrelated to objectively assessed performance on an attentional network task. Poor attentional network performance was a significant predictor of traffic violations but not of accidents. To increase traffic safety, both symptoms of ADHD and attentional network performance appear to merit particular attention.

Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis

Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or non-linear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined electroencephalogram (EEG)/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even "traditional," highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g., from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate modeling ("regression-ERPs"), linear deconvolution modeling, and non-linear modeling with the generalized additive model into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g., Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses. It is available as open-source software at http://www.unfoldtoolbox.org.