Single-trial EEG Analysis Research Articles

Single-trial EEG analysis reveals burst structure during photic driving

ObjectivePhotic driving in the human visual cortex evoked by intermittent photic stimulation is usually characterized in averaged data by an ongoing oscillation showing frequency entrainment and resonance phenomena during the course of stimulation. We challenge this view of an ongoing oscillation by analyzing unaveraged data. Methods64-channel EEGs were recorded during visual stimulation with light flashes at eight stimulation frequencies between 7.8 and 23 Hz for fourteen healthy volunteers. Time-frequency analyses were performed in averaged and unaveraged data. ResultsWhile we find ongoing oscillations in the averaged data during intermittent photic stimulation, we find transient events (bursts) of activity in the unaveraged data. Both resonance and entrainment occur for the ongoing oscillations in the averaged data and the bursts in the unaveraged data. ConclusionsWe argue that the continuous oscillations in the averaged signal may be composed of brief, transient bursts in single trials. Our results can also explain previously observed amplitude fluctuations in averaged photic driving data. SignificanceSingle-trial analyses might consequently improve our understanding of resonance and entrainment phenomena in the brain.

N170 component analysis of single-trial EEG based on electrophysiological source imaging.

Event-related potentials (ERP) are brain-evoked potentials that reflect the neural activity of the brain. However, it is difficult to isolate the ERP components of our interest because single-trial EEG is disturbed by other signals, and the average ERP analysis in turn loses single-trial information. In this paper, we used electrophysiological source imaging (ESI) to analyze the N170 component of single-trial EEG triggered by face stimulation. The results show that ESI is feasible for the analysis of N170 and that there are left-right differences in the area of the fusiform gyrus associated with face stimulation in the brain. Clinical Relevance- Analysis of the N170 of single-trial EEG by ESI may help in the diagnosis of patients with prosopagnosia and may also help physicians clinically in determining whether the fusiform gyrus region is damaged.

Can the Brain Build Probability Distributions?

How humans efficiently operate in a world with massive amounts of data that need to be processed, stored, and recalled has long been an unsettled question. Our physical and social environment needs to be represented in a structured way, which could be achieved by reducing input to latent variables in the form of probability distributions, as proposed by influential, probabilistic accounts of cognition and perception. However, few studies have investigated the neural processes underlying the brain’s potential ability to represent a probability distribution’s complex, global features. Here, we presented participants with a sequence of tones that formed a normal or a bimodal distribution. Using a novel, single-trial EEG analysis, we demonstrate a neural response that indexes the likelihood of an item, given previously presented items, and corresponds to the experienced tones’ distribution. Our results indicate that the adult human brain can build a representation of the complex, global pattern of a probability distribution and offer a novel tool for an in-depth understanding of related neural mechanics.

Neural anticipatory mechanisms predict faster reaction times and higher fluid intelligence.

Higher cognitive ability is reliably linked to better performance on chronometric tasks (i.e., faster reaction times, RT), yet the neural basis of these effects remains unclear. Anticipatory processes represent compelling yet understudied potential mechanisms of these effects, which may facilitate performance through reducing the uncertainty surrounding the temporal onset of stimuli (temporal uncertainty) and/or facilitating motor readiness despite uncertainty about impending target locations (target uncertainty). Specifically, the contingent negative variation (CNV) represents a compelling candidate mechanism of anticipatory motor planning, while the alpha oscillation is thought to be sensitive to temporal contingencies in perceptual systems. The current study undertook a secondary analysis of a large data set (n=91) containing choice RT, cognitive ability, and EEG measurements to help clarify these issues. Single-trial EEG analysis in conjunction with mixed-effects modeling revealed that higher fluid intelligence corresponded to faster RT on average. When considered together, temporal and target uncertainty moderated the RT-ability relationship, with higher ability being associated with greater resilience to both types of uncertainty. Target uncertainty attenuated the amplitude of the CNV for all participants, but higher ability individuals were more resilient to this effect. Similarly, only higher ability individuals showed increased prestimulus alpha power (at left-lateralized sites) during longer, more easily anticipated interstimulus intervals. Collectively, these findings emphasize top-down anticipatory processes as likely contributors to chronometry-ability correlations.

A single trial analysis of EEG in recognition memory: Tracking the neural correlates of memory strength

Recent work in perceptual decision-making has shown that although two distinct neural components differentiate experimental conditions (e.g., did you see a face or a car), only one tracked the evidence guiding the decision process. In the memory literature, there is a distinction between a fronto-central evoked potential measured with EEG beginning at 350ms that seems to track familiarity and a late parietal evoked potential that peaks at 600ms that tracks recollection. Here, we applied single-trial regressor analysis (similar to multivariate pattern analysis, MVPA) and diffusion decision modeling to EEG and behavioral data from two recognition memory experiments to test whether these two components contribute to the recognition decision process. The regressor analysis only involved whether an item was studied or not and did not involve any use of the behavioral data. Only late EEG activity distinguishes studied from not studied items that peaks at about 600ms following each test item onset predicted the diffusion model drift rate derived from the behavioral choice and reaction times (but only for studied items). When drift rate was made a linear function of the trial-level regressor values, the estimate for studied items was different than zero. This showed that the later EEG activity indexed the trial-to-trial variability in drift rate for studied items. Our results provide strong evidence that only a single EEG component reflects evidence being used in the recegnition decision process.

RETRACTED: P02. Predicting the outcome of post-anoxic comatose patients based on single-trial EEG analysis

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This Abstract has been retracted at the request of the co-authors due to the fact that the Abstract was prepared and submitted by a single author, without the agreement of other co-authors, includes several mistakes and potentially conflicts with another publication.

You Can't Think and Hit at the Same Time: Neural Correlates of Baseball Pitch Classification.

Hitting a baseball is often described as the most difficult thing to do in sports. A key aptitude of a good hitter is the ability to determine which pitch is coming. This rapid decision requires the batter to make a judgment in a fraction of a second based largely on the trajectory and spin of the ball. When does this decision occur relative to the ball’s trajectory and is it possible to identify neural correlates that represent how the decision evolves over a split second? Using single-trial analysis of electroencephalography (EEG) we address this question within the context of subjects discriminating three types of pitches (fastball, curveball, slider) based on pitch trajectories. We find clear neural signatures of pitch classification and, using signal detection theory, we identify the times of discrimination on a trial-to-trial basis. Based on these neural signatures we estimate neural discrimination distributions as a function of the distance the ball is from the plate. We find all three pitches yield unique distributions, namely the timing of the discriminating neural signatures relative to the position of the ball in its trajectory. For instance, fastballs are discriminated at the earliest points in their trajectory, relative to the two other pitches, which is consistent with the need for some constant time to generate and execute the motor plan for the swing (or inhibition of the swing). We also find incorrect discrimination of a pitch (errors) yields neural sources in Brodmann Area 10, which has been implicated in prospective memory, recall, and task difficulty. In summary, we show that single-trial analysis of EEG yields informative distributions of the relative point in a baseball’s trajectory when the batter makes a decision on which pitch is coming.

Temporal dynamics of prediction error processing during reward-based decision making

Adaptive decision making depends on the accurate representation of rewards associated with potential choices. These representations can be acquired with reinforcement learning (RL) mechanisms, which use the prediction error (PE, the difference between expected and received rewards) as a learning signal to update reward expectations. While EEG experiments have highlighted the role of feedback-related potentials during performance monitoring, important questions about the temporal sequence of feedback processing and the specific function of feedback-related potentials during reward-based decision making remain. Here, we hypothesized that feedback processing starts with a qualitative evaluation of outcome-valence, which is subsequently complemented by a quantitative representation of PE magnitude. Results of a model-based single-trial analysis of EEG data collected during a reversal learning task showed that around 220ms after feedback outcomes are initially evaluated categorically with respect to their valence (positive vs. negative). Around 300ms, and parallel to the maintained valence-evaluation, the brain also represents quantitative information about PE magnitude, thus providing the complete information needed to update reward expectations and to guide adaptive decision making. Importantly, our single-trial EEG analysis based on PEs from an RL model showed that the feedback-related potentials do not merely reflect error awareness, but rather quantitative information crucial for learning reward contingencies.

Detecting Typing Errors in a Numerical Typing Task with Linear Discriminant Analysis of Single Trial EEG

Detecting Typing Errors in a Numerical Typing Task with Linear Discriminant Analysis of Single Trial EEG

Detecting Typing Errors in a Numerical Typing Task with Linear Discriminant Analysis of Single Trial EEG

Linear Discriminant Analysis (LDA) was utilized to detect numerical typing errors in the context of daily data input. Single EEG trial data from 6 subjects were analyzed and a 67% detection rate was demonstrated by Fisher LDA classifier with an optimal Mahalanobis distance ratio setting. Sensitivity analysis showed that Fisher LDA classifier detected the errors in terms of 9-digit numbers by 62.19% on average, in comparison with 3.33% and 47.22% using the prior model and the chance model. This is one step towards predicting human errors in perceptual-motor tasks before their occurrence; future work would focus on benchmarking to improve current method toward an online and robust classifier.

Cortically coupled computer vision for rapid image search

We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.

Temporal Characterization of the Neural Correlates of Perceptual Decision Making in the Human Brain

Single and multi-unit recordings in primates have identified spatially localized neuronal activity correlating with an animal's behavioral performance. Due to the invasive nature of these experiments, it has been difficult to identify such correlates in humans. We report the first non-invasive neural measurements of perceptual decision making, via single-trial EEG analysis, that lead to neurometric functions predictive of psychophysical performance for a face versus car categorization task. We identified two major discriminating components. The earliest correlating with psychophysical performance was consistent with the well-known face-selective N170. The second component, which was a better match to the psychometric function, did not occur until at least 130 ms later. As evidence for faces versus cars decreased, onset of the later, but not the earlier, component systematically shifted forward in time. In addition, a choice probability analysis indicated strong correlation between the neural responses of the later component and our subjects' behavioral judgements. These findings demonstrate a temporal evolution of component activity indicative of an evidence accumulation process which begins after early visual perception and has a processing time that depends on the strength of the evidence.