Single-trial Event-related Potentials Research Articles

Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks.

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and the identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 62% for known items and 66% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials

BackgroundIntra-individual variability (IIV), a measure of variance within an individual’s performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging. New methodsBased on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels. ResultsWe found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%. Comparison with existing methodsThe IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue. ConclusionsThis study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.

Transdiagnostic psychopathology in the light of robust single-trial event-related potentials.

Recent evidence indicates that event-related potentials (ERPs) as measured on the electroencephalogram (EEG) are more closely related to transdiagnostic, dimensional measures of psychopathology (TDP) than to diagnostic categories. A comprehensive examination of correlations between well-studied ERPs and measures of TDP is called for. In this study, we recruited 50 patients with emotional disorders undergoing 14 weeks of transdiagnostic group psychotherapy as well as 37 healthy comparison subjects (HC) matched in age and sex. HCs were assessed once and patients three times throughout treatment (N = 172 data sets) with a battery of well-studied ERPs and psychopathology measures consistent with the TDP framework The Hierarchical Taxonomy of Psychopathology (HiTOP). ERPs were quantified using robust single-trial analysis (RSTA) methods and TDP correlations with linear regression models as implemented in the EEGLAB toolbox LIMO EEG. We found correlations at several levels of the HiTOP hierarchy. Among these, a reduced P3b was associated with the general p-factor. A reduced error-related negativity correlated strongly with worse symptomatology across the Internalizing spectrum. Increases in the correct-related negativity correlated with symptoms loading unto the Distress subfactor in the HiTOP. The Flanker N2 was related to specific symptoms of Intrusive Cognitions and Traumatic Re-experiencing and the mismatch negativity to maladaptive personality traits at the lowest levels of the HiTOP hierarchy. Our study highlights the advantages of RSTA methods and of using validated TDP constructs within a consistent framework. Future studies could utilize machine learning methods to predict TDP from a set of ERP features at the subject level.

Robust single-trial event-related potentials differentiate between distress and fear disorders.

Recent evidence indicates that measures of brain functioning as indexed by event-related potentials (ERPs) on the electroencephalogram align more closely to transdiagnostic measures of psychopathology than to categorical taxonomies. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a transdiagnostic, dimensional framework aiming to solve issues of comorbidity, symptom heterogeneity, and arbitrary diagnostic boundaries. Based on shared features, the emotional disorders are allocated into subfactors Distress and Fear. Evidence indicates that disorders that are close in the HiTOP hierarchy share etiology, symptom profiles, and treatment outcomes. However, further studies testing the biological underpinnings of the HiTOP are called for. In this study, we assessed differences between Distress and Fear in a range of well-studied ERP components. In total, 50 patients with emotional disorders were divided into two groups (Distress, N = 25; Fear, N = 25) according to HiTOP criteria and compared against 37 healthy comparison (HC) subjects. Addressing issues in traditional ERP preprocessing and analysis methods, we applied robust single-trial analysis as implemented in the EEGLAB toolbox LIMO EEG. Several ERP components were found to differ between the groups. Surprisingly, we found no difference between Fear and HC for any of the ERPs. This suggests that some well-established results from the literature, e.g., increased error-related negativity in OCD, are not a shared neurobiological correlate of the Fear subfactor. Conversely, for Distress, we found reductions compared to Fear and HC in several ERP components across paradigms. Future studies could utilize HiTOP-validated psychopathology measures to more precisely define subfactor groups.

Effects of interstimulus interval and significance on electrodermal and central measures of the phasic orienting reflex (OR) in a dishabituation task

Although the P300 event-related potential (ERP) is the most likely central measure of Sokolov’s Orienting Reflex (OR), there are few systematic comparisons with the skin conductance response (SCR), the “gold standard” electrodermal OR measure. We examine habituation, stimulus significance, and inter-stimulus interval (ISI) effects in SCRs and components of the P300 from single-trial ERPs in an auditory dishabituation paradigm. Single trial ERP components were separated by temporal principal components analysis, and five components of the P300 were examined as potential phasic OR measures: P3a, P3b, Novelty P3, and two Slow Waves (SW1, SW2). Across the factors of ISI and significance, SCRs showed decrement over trials, recovery at a deviant, and dishabituation at the subsequent standard. This general pattern was not present in any of the components of the P300. SCRs were also larger to significant stimuli and at the long ISI; effects differed between P300 components. The electrodermal SCR showed the complete profile over trials expected of the phasic OR, and was enhanced by stimulus significance, confirming it as the model measure of Sokolov’s phasic OR. Components of the P300 failed to match this profile, but instead appear to reflect different aspects of the stimulus processing involved in OR elicitation.

Real‐time Neurofeedback Rewards Memory‐related Potentials in Older Brains

AbstractBackgroundElectroencephalogram (EEG)‐based neurofeedback (NF) allows reward of desired brain activity in milliseconds. Compared to frequency power‐based approach during resting state EEG, event‐related potentials (ERPs) based NF during a task is technically challenging because of the cost of computation over a time window. We have previously established that left‐frontal memory‐related potentials discriminate normal cognitive aging from age‐ and education‐ matched older adults with risk of mild cognitive impairment (MCI). To modulate brainwaves during working memory retrieval towards healthier patterns, we develop an ERP‐based neurofeedback protocol to reward better memory‐related potentials.MethodThe training protocol is developed based on a modified delayed match‐to‐sample task also known as Bluegrass working memory paradigm. During each memory trial, a participant holds the sample target image in his/her working memory and responds whether each subsequent image is a Match or Nonmatch to the memory target by pressing the button. Real‐time classification of extracted ERP features is analyzed after stimulus onset, the dynamic machine‐learning program then modifies the stimulus by turning it into vivid color (reward) or leaving it with black‐and‐white (unrewarded). The processing pipeline has been developed using ERPs of older adults with and without MCI. The left frontal sites (F3 and F7) were preprocessed by re‐referencing and filtering with a 0.05 – 40 Hz band‐pass filter. For each participant, the EEG of 5 offline base trials in a 300‐600ms time window was used as a baseline. The ERP difference of the baseline versus the subsequent single trial is calculated to determine whether the image will be colored or left at black‐and‐white.ResultOur newly developed ERP‐neurofeedback protocol has succeeded in providing visual feedback based on fluctuation of single trial ERP in real time. The program was also designed to fit in each individual due to varied sensibility to NF. Currently we are to examine the feasibility and effectiveness of the proposed protocol by measuring memory indicators before and after NF training sessions.ConclusionWe have developed a new protocol using ERP‐based neurofeedback to improve personalized memory retrieval. The program is the first step to intervention trials for enhancing successful memory by rewarding desired memory‐related potentials in older brains.

Characterization of attentional event-related potential from REM sleep behavior disorder patients based on explainable machine learning

Background and objectiveIdiopathic rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of neurodegeneration and is associated with cortical dysfunction. The purpose of this study was to investigate the spatiotemporal characteristics of cortical activities underlying impaired visuospatial attention in iRBD patients using an explainable machine-learning approach. MethodsAn algorithm based on a convolutional neural network (CNN) was devised to discriminate cortical current source activities of iRBD patients due to single-trial event-related potentials (ERPs), from those of normal controls. The ERPs from 16 iRBD patients and 19 age- and sex-matched normal controls were recorded while the subjects were performing visuospatial attentional task, and converted to two-dimensional images representing current source densities on flattened cortical surface. The CNN classifier was trained based on overall data, and then, a transfer learning approach was applied for the fine-tuning to each patient. ResultsThe trained classifier yielded high classification accuracy. The critical features for the classification were determined by layer-wise relevance propagation, so that the spatiotemporal characteristics of cortical activities that were most relevant to cognitive impairment in iRBD were revealed. ConclusionsThese results suggest that the recognized dysfunction in visuospatial attention of iRBD patients originates from neural activity impairment in relevant cortical regions and may contribute to the development of useful iRBD biomarkers based on neural activity.

Development of a Cognitive Training System Based on Decoding of Single-Trial Event-Related Potentials

Our final goal is to promote brain health by developing a brain-machine interface (BMI)-based cognitive training device. We have been developing a cognitive level assessment device called the "Neurotrainer," which uses event-related potential (ERP) analysis. This device was operated by decoding single-trial ERPs using pattern recognition. Here, we extended our method to decoding single-trial ERPs during a racing game with 8 choices of characters. The decoding accuracy of the target character was about 54% for a single trial and 83% for a cumulative total of five trials (chance level = 12.5%). Also, the ERP responses were stronger in the feedback condition than in the no-feedback condition. These results suggest that the BMI might be an effective tool for cognitive training, in which real-time neurofeedback affects the activation of the brain of the players.

Phase Analysis of Event-Related Potentials Based on Dynamic Mode Decomposition

Real-time detection of event-related potentials (ERPs) and exploration of ERPs generation mechanisms are vital to practical application of brain–computer interfaces (BCI). Traditional methods for ERPs analysis often fall into time domain, time–frequency domain, or spatial domain. Methods which can reveal spatiotemporal interactions by simultaneously analyzing multi-channel EEG signals may provide new insights into ERP research and is highly desired. Additionally, although phase information has been investigated to describe the phase consistency of a certain frequency component across different ERP trials, it is of research significance to analyze the phase reorganization across different frequency components that constitute a single-trial ERP signal. To address these problems, dynamic mode decomposition (DMD) was applied to decompose multi-channel EEG into a series of spatial–temporal coherent DMD modes, and a new metric, called phase variance distribution (PVD) is proposed as an index of the phase reorganization of DMD modes during the ERP in a single trial. Based on the PVD, a new error-related potential (ErrP) detection method based on symmetric positive defined in Riemann manifold is proposed to demonstrate the significant PVD differences between correct and error trials. By including the phase reorganization index, the 10-fold cross-validation results of an ErrP detection task showed that the proposed method is 4.98%, 27.99% and 7.98% higher than the counterpart waveform-based ErrP detection method in the terms of weighted accuracy rate, precision and recall of the ErrP class, respectively. The resulting PVD curve shows that with the occurrence of ERP peaks, the phases of different frequency rhythms are getting to aligned and yields a significant smaller PVD. Since the DMD modes of different frequencies characterize spatiotemporal coherence of multi-channel EEG at different functional regions, the new phase reorganization index, PVD, may indicate the instantaneous phase alignment of different functional networks and sheds light on a new interpretation of ERP generation mechanism.

Explainable Machine-Learning-Based Characterization of Abnormal Cortical Activities for Working Memory of Restless Legs Syndrome Patients.

Restless legs syndrome (RLS) is a sensorimotor disorder accompanied by a strong urge to move the legs and an unpleasant sensation in the legs, and is known to accompany prefrontal dysfunction. Here, we aimed to clarify the neural mechanism of working memory deficits associated with RLS using machine-learning-based analysis of single-trial neural activities. A convolutional neural network classifier was developed to discriminate the cortical activities between RLS patients and normal controls. A layer-wise relevance propagation was applied to the trained classifier in order to determine the critical nodes in the input layer for the output decision, i.e., the time/location of cortical activities discriminating RLS patients and normal controls during working memory tasks. Our method provided high classification accuracy (~94%) from single-trial event-related potentials, which are known to suffer from high inter-trial/inter-subject variation and low signal-to-noise ratio, after strict separation of training/test/validation data according to leave-one-subject-out cross-validation. The determined critical areas overlapped with the cortical substrates of working memory, and the neural activities in these areas were correlated with some significant clinical scores of RLS.

A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN.

ObjectiveBrain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy.ApproachIn this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights.Main resultsThe performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05).SignificanceThe proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.

The importance of ocular artifact removal in single-trial ERP analysis: The case of the N250 in face learning

ObjectiveSingle-trial event-related potentials (ERPs) offer fine-grained information about the trajectories of the neurocognitive processes but are highly sensitive to any artifacts in the EEG signal. The primary aim of this study was to assess the impact of ocular artifact removal on the single-trial N250 ERP analysis of face learning in individual participants. MethodsWe present a detailed description of our research-grade EEG hardware setup and a highly reproducible code (https://osf.io/aqhmn/) for generating time series of single-trial N250 ERP amplitudes and precise identification of a changepoint between face memory trace acquisition and maintenance. Ocular artifacts were removed using a new semi-automatic approach with only one hyperparameter based on the correlation between EEG components from independent component analysis (ICA) and the EOG signal. ResultsResults from the simulation study showed that our ocular artifact filtration decreased the average RMSE by half and achieved the highest increase of SNR among all the compared methods. It decreased standard deviations and improved the fit of the broken-line regression models for all participants by 25% ± 17% (min. 2%, max. 63%). Conclusions and significanceOcular artifact filtration had a substantial positive impact on the regression modeling of single-trial ERP amplitudes. Lack of ocular artifact removal can drastically distort the conclusions about the face learning process from single-trial N250 ERP experiments for individual participants. The changepoint locations changed for 13 out of 15 participants. This is the first published analysis of time series of single-trial N250 ERP amplitudes in face learning.

The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults

BackgroundOne of the goals of neuropsychology is to understand the brain mechanisms underlying aspects of attention and cognitive control. Several tasks have been developed as a part of this body of research, however their results are not always consistent. A reliable comparison of the data and a synthesis of study conclusions has been precluded by multiple methodological differences. Here, we describe a publicly available, high-density electroencephalography (EEG) dataset obtained from 42 healthy young adults while they performed 3 cognitive tasks: (i) an extended multi-source interference task; (ii) a 3-stimuli oddball task; (iii) a control, simple reaction task; and (iv) a resting-state protocol. Demographic and psychometric information are included within the dataset.Dataset ValidationFirst, data validation confirmed acceptable quality of the obtained EEG signals. Typical event-related potential (ERP) waveforms were obtained, as expected for attention and cognitive control tasks (i.e., N200, P300, N450). Behavioral results showed the expected progression of reaction times and error rates, which confirmed the effectiveness of the applied paradigms.ConclusionsThis dataset is well suited for neuropsychological research regarding common and distinct mechanisms involved in different cognitive tasks. Using this dataset, researchers can compare a wide range of classical EEG/ERP features across tasks for any selected subset of electrodes. At the same time, 128-channel EEG recording allows for source localization and detailed connectivity studies. Neurophysiological measures can be correlated with additional psychometric data obtained from the same participants. This dataset can also be used to develop and verify novel analytical and classification approaches that can advance the field of deep/machine learning algorithms, recognition of single-trial ERP responses to different task conditions, and detection of EEG/ERP features for use in brain-computer interface applications.

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation.

Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.

Disentangling effects of expectancy, accuracy, and empathy on the processing of observed actions.

A number of studies suggest that event-related potential (ERP) components previously associated with error processing might represent expectation violation instead of valence. When observing others, these processes might further be modulated by trait empathy. We suggest that trait empathy modulates expectancy formation and that these expectancies then influence observed response processing as reflected in a frontocentral negative ERP component resembling the previously described observer error-related negativity. We acquired single trial ERPs of participants who observed another person in a true- or false-belief condition answering correctly or erroneously. Additionally, we prompted participants' expectancy in some trials. Using linear mixed model analyses, we found that for low empathy participants, expectations for the false-belief condition decreased throughout the experiment, so that expectations were more pronounced in participants with higher empathy toward the end of the experiment. We also found that single trial expectancy measures derived from regression models of the measured expectancies predicted the amplitude of the frontocentral negative ERP component, and that neither the addition of empathy nor accuracy or trial type (true- or false-belief) led to the explanation of significantly more variance compared with the model just containing expectancy as predictor. These results suggest that empathy modulates the processing of observed responses indirectly via its effect on expectancy of the response.

Quantum Theory of EEG with Application to the Single-Trial ERP Analysis

The probabilistic formalism of quantum mechanics is used to quantitatively link the electroencephalogram (EEG) with the underlying microscale activity of cortical neurons. Previous approaches applied methods of classic physics to reconstruct the EEG in terms of explicit physical models of cortical neurons and the volume conductor. However, the multiplicity of cellular processes with extremely intricate mixtures of deterministic and random factors prevented the creation of consistent biophysical parameter sets. To avoid the uncertainty surrounding the physical attributes of the neuronal ensembles, we undertake here a radical departure from deterministic equations of classical physics to the probabilistic reasoning of quantum mechanics. The crucial step is the relocation of the elementary bioelectric sources from cellular to molecular level. Using a novel method of time-frequency analysis with adaptive segmentation for digital processing of empirical EEG and single trial event related potentials (ERPs), we found universal “building blocks” of these cortical processes both in the frequency and time domains. This result is qualified as a phenomenon known in statistical physics and quantum mechanics as universality. Therationale is that despite dramatic differences in the cellular machineries, the statistical factors governed by the central limit theorem produce the EEG waveform as a statistical aggregate of the synchronized activities of large ensembles of closely located cortical neurons. Using these theoretical and empirical findings the probabilistic laws that control the microscale machinery generating the EEG are deduced.

A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid Serial Visual Presentation.

Brain-computer interfaces (BCIs) based on rapid serial visual presentation (RSVP) have been widely used to categorize target and non-target images. However, it is still a challenge to detect single-trial event related potentials (ERPs) from electroencephalography (EEG) signals. Besides, the variability of EEG signal over time may cause difficulties of calibration in long-term system use. Recently, collaborative BCIs have been proposed to improve the overall BCI performance by fusing brain activities acquired from multiple subjects. For both individual and collaborative BCIs, feature extraction and classification algorithms that can be transferred across sessions can significantly facilitate system calibration. Although open datasets are highly efficient for developing algorithms, currently there is still a lack of datasets for a collaborative RSVP-based BCI. This paper presents a cross-session EEG dataset of a collaborative RSVP-based BCI system from 14 subjects, who were divided into seven groups. In collaborative BCI experiments, two subjects did the same target image detection tasks synchronously. All subjects participated in the same experiment twice with an average interval of ∼23 days. The results in data evaluation indicate that adequate signal processing algorithms can greatly enhance the cross-session BCI performance in both individual and collaborative conditions. Besides, compared with individual BCIs, the collaborative methods that fuse information from multiple subjects obtain significantly improved BCI performance. This dataset can be used for developing more efficient algorithms to enhance performance and practicality of a collaborative RSVP-based BCI system.

Evoked Brain Responses in Odor Stimuli Evaluation - an EEG Event Related Potential Study.

Decoding olfactory cognition has been generating significant interest in recent years due to a wide range of applications, from diagnosing neurodegenerative disorders to consumer research and traditional medicine. In this study, we have investigated whether changes in odor stimuli evaluation across repeated stimuli presentation can be attributed to changes in brain perception of the stimuli. Epoch intervals representing olfactory sensory perception were extracted from electroencephalography (EEG) signals using minimum variance distortionless response (MVDR)-based single trial event related potential (ERP) approach to understand the evoked response to high pleasantness and low pleasantness stimuli. We found statistically significant changes in self reported stimuli evaluation between initial and final trials (p < 0.05) for both stimuli categories. However, the changes in ERP amplitude were found to be statistically significant only for the high pleasantness stimuli. This implies that olfactory stimuli of higher hedonic value recruit high-order cognitive processing that may be responsible for initial increased ERP response, as well as for rapid subsequent adaptation in processing the stimuli.

Denoising improves visualization of evoked potentials with limited number of trials

Ensemble averaging is typically used to enhance the signal to noise ratio (SNR) of event related potentials (ERPs). This approach has the disadvantage of extending the recording time. We present an alternative approach to achieve SNRs similar to the ones obtained with ensemble averaging but with fewer trials. Single trial ERPs were denoised by using a wavelet denoising technique. With simulated ERPs we compared the performance obtained with the standard and denoised averages by quantifying the root-mean-square-error (RMSE) between ensemble averages obtained with and without denoising. Denoising decreased the RMSE, giving similar values to the ones obtained with the standard averages but with less than half the number of trials. RMSEs were comparable for larger number of trials, thus showing that denoising did not introduce spurious distortions to the ERPs. Wavelet denoising proved to be an efficient technique to improve evoked potential SNRs. It is useful in clinical settings, where recordings may be constrained by the limited capability to obtain a sufficient number of trials.

Lexical access versus lexical decision processes for auditory, visual, and audiovisual items: Insights from behavioral and neural measures

In two experiments, we investigated the relationship between lexical access processes, and processes that are specifically related to making lexical decisions. In Experiment 1, participants performed a standard lexical decision task in which they had to respond as quickly and as accurately as possible to visual (written), auditory (spoken) and audiovisual (written + spoken) items. In Experiment 2, a different group of participants performed the same task but were required to make responses after a delay. Linear mixed effect models on reaction times and single trial Event-Related Potentials (ERPs) revealed that ERP lexicality effects started earlier in the visual than auditory modality, and that effects were driven by the written input in the audiovisual modality. More negative ERP amplitudes predicted slower reaction times in all modalities in both experiments. However, these predictive amplitudes were mainly observed within the window of the lexicality effect in Experiment 1 (the speeded task), and shifted to post-response-probe time windows in Experiment 2 (the delayed task). The lexicality effects lasted longer in Experiment 1 than in Experiment 2, and in the delayed task, we additionally observed a “re-instantiation” of the lexicality effect related to the delayed response. Delaying the response in an otherwise identical lexical decision task thus allowed us to separate lexical access processes from processes specific to lexical decision.