Characteristics Of Event-related Potentials Research Articles

The Neural Correlates of Implicit Cognitive Bias Toward Internet-Related Cues in Internet Addiction: An ERP Study.

Internet addiction is a sort of non-psychoactive substance dependence. The Implicit Association Test (IAT) is used to measure implicit cognition. Event-related potential (ERP) is one of the most widely used methods in cognitive neuroscience research to investigate the physiological correlates of cognitive activity associated with processing information. Further investigating the ERP characteristics of implicit cognitive bias in Internet addiction would be helpful in understanding the nature of Internet addiction. This study investigated the ERP characteristics of implicit cognitive bias in Internet addiction. The participants included 60 Internet-addicted individuals (IAG) and 60 normal controls (NCG). All participants were measured with ERPs using the IAT. The results showed that there was a significant difference in the Internet-related IAT effect for reaction times between IAG and NCG, and there were stronger positive implicit associations toward Internet related cues in IAG than NCG. Using P1, N2, P3, and N4 as dependent variables, a mixed repeated-measures analysis of variance (ANOVA) on the mean latencies and mean amplitudes revealed a significant interaction between the groups (IAG vs. NCG) and stimulus condition (compatible trials vs. incompatible trials) for the N2 and P3 amplitudes; the simple effects analysis showed that the N2 and P3 amplitudes were larger under the IAG-compatible trial conditions than under the IAG-incompatible trial conditions. In the IAG group, the positive implicit associations with Internet-related cues elicited larger N2 and P3 amplitudes at the occipital lobe sites. These results indicated that Internet addictive individuals show stronger positive implicit associations toward Internet-related cues, and the positive implicit associations toward Internet-related cues elicited ERP changes at occipital lobe sites.

Identification of Attention Deficit/Hyperactivity Disorder in Children Using Multiple ERP Features

Background and Objective: Attention deficit hyperactivity disorder (ADHD) is a typical neurodevelopmental disorder occurs in children’s early school-age, which often results in serious executive dysfunction. Recent ADHD studies highlight the great potential of non-invasive event-related potential (ERP) technique. It is thus worth combining multiple features to form sensitive and robust biomarkers to distinguish ADHD from normal children. Methods: In this paper, we collected the EEG signals of sixty-eight ADHD children and seventy-three age-match typically developing children during a classic Simon-spatial Stroop task. A channel optimization method was used to select the feature channel. Time-domain features and frequencydomain features were extracted from EEG data. Three classifiers were used to classify ADHD children from typically developing children by using multiple features as well as each single feature. Results: ADHD children showed weaker N2 and P2 signals than typically developing children. Behavior response results showed that, children with ADHD exhibited lower correct response rates, longer average response time and higher data variance. In classification experiment, performance of three classifiers trained on multiple features was much better than that on single feature. Multiple features classification achieved the highest accuracy of 96.6%, while single time-domain and frequencydomain feature only achieved the highest accuracy of 88.10% and 92.85% respectively. All the highest accuracies were achieved on feature channel in inferior parietal cortex. Conclusion: Feature channel generally performed better than empirical channel. The multiple ERP features classification method has a good recognition accuracy, being worth researching in ADHD’s auxiliary diagnosis. Keywords: Attention deficit/hyperactivity disorder, executive function, electroencephalogram, feature extraction, classification, event related potential.

Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition

Background:Preprocessed Event-related potential (ERP) data are usually organized in multi-way tensor, in which tensor decomposition serves as a powerful tool for data processing. Due to the limitation of computation burden for multi-way data and the low algorithm performance of stability and efficiency, multi-way ERP data are conventionally reorganized into low-order tensor or matrix before further analysis. However, the reorganization may hamper mode specification and spoil the interaction information among different modes. New Method:In this study, we applied a fifth-order tensor decomposition to a set of fifth-order ERP data collected by exerting proprioceptive stimulus on left and right hand. One of the latest nonnegative CANDECOMP/PARAFAC (NCP) decomposition methods implemented by alternating proximal gradient (APG) was employed. We also proposed an improved DIFFIT method to select the optimal component number for the fifth-order tensor decomposition. Results:By the fifth-order NCP model with a proper component number, the ERP data were fully decomposed into spatial, spectral, temporal, subject and condition factors in each component. The results showed more pairs of components with symmetric activation region in left and right hemisphere elicited by contralateral stimuli on hand. Comparison with Existing Method(s):In our experiment, more interesting components and coherent brain activities were extracted, compared with previous studies. Conclusions:The discovered activities elicited by proprioceptive stimulus are in line with those in relevant cognitive neuroscience studies. Our proposed method has proved to be appropriate and viable for processing high-order EEG data with well-preserved interaction information among all modes.

Detecting Concealed Information with Fused Electroencephalography and Functional Near-infrared Spectroscopy

In this study, fused electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques were utilized to examine the relationship between the ERP (event-related potential) component P300 and fNIRS hemodynamic signals for high-accuracy deception detection. During the performance of a modified concealed information test (CIT) task, a series of Chinese names were presented, which served as the target, irrelevant, or the probe stimuli for both the guilty and innocent groups. For participants in the guilty group, the probe stimulus was their individual name, whereas for the innocent group, the probe stimulus was one irrelevant name. In particular, data from concurrent fNIRS and ERP recordings were carefully inspected for participants from the two groups. Interestingly, we discovered that for the guilty group, the probe stimulus elicited significantly higher P300 amplitude at parietal site and also evoked significantly stronger oxyhemoglobin (HbO) concentration changes in the bilateral superior frontal gyrus and bilateral middle frontal gyrus than the irrelevant stimuli. However, this is not the case for the innocent group, in which participants exhibited no significant differences in both ERP and fNIRS measures between the probe and irrelevant stimuli. More importantly, our findings also demonstrated that the combined ERP and fNIRS feature was able to differentiate the guilty and innocent groups with enhanced sensitivity, in which AUC (the area under Receiver Operating Characteristic curve) is 0.94 for deception detection based on the combined indicator, much higher than that based on the ERP component P300 only (0.85) or HbO measure only (0.84).

Event-related potential (ERP) correlates of face processing in verbal children with autism spectrum disorders (ASD) and their first-degree relatives: a family study

BackgroundInherited abnormalities of perception, recognition, and attention to faces have been implicated in the etiology of autism spectrum disorders (ASD) including abnormal components of event-related brain potentials (ERP) elicited by faces.MethodsWe examined familial aggregation of face processing ERP abnormalities previously implicated in ASD in 49 verbal individuals with ASD, 36 unaffected siblings (US), 18 unaffected fathers (UF), and 53 unrelated controls (UC). The ASD, US, and UC groups ranged in age from 12 to 21 years, the UF group ranged in age from 30 to 56 years. ERP responses to images of upright and inverted faces and houses were analyzed under disparate EEG reference schemes.ResultsFace-sensitive features of N170 and P1 were readily observed in all groups. Differences between ASD and control groups depended upon the EEG reference scheme. Notably, the superiority of face over object for N170 latency was attenuated in ASD subjects, but not their relatives; this occurred exclusively with the average reference. The difference in N170 amplitude between inverted and upright faces was reduced in both ASD and US groups relative to UC, but this effect was significant only with the vertex reference. Furthermore, similar group differences were observed for both inverted faces and inverted houses, suggesting a lack of face specificity for the attenuation of the N170 inversion effect in ASD.ConclusionThe present findings refine understanding of face processing ERPs in ASD. These data provide only modest evidence for highly-selective ASD-sensitive ERP features, and underscore the sensitivity of these biomarkers to ERP reference scheme. These schemes have varied across published studies and must be accounted for in future studies of the relationship between these commonly acquired ERP characteristics, genotype, and ASD.

Assessment of the Parameters of Cognitive Potentials in Adolescent Northerners in Health and Developmental Delays

Components of event-related potentials (ERP) were studied using an active sound signal discrimination task in northern schoolchildren aged 12–18 years with impaired mental development and learning difficulties (IQ 65 ± 4) and without mental developmental impairments (IQ 90 ± 10). The peak latency of the P300 component in the central and parietal areas of the cortex on perception and discrimination of target sound stimuli was greater in northern children with impaired mental development (Cz 370 ± 36 msec, 8.8 ± 4.1 μV; Pz 375 ± 39 msec, 10.2 ± 3.3 μV) as compared with their normally developing peers (339 ± 28 msec, 10.4 ± 3.0 μV; 340 ± 35 msec, 12.2 ± 2.2 μV, respectively). Negative correlations were found between cognitive abilities, the accuracy and speed of information processing in an attention task, and the latency of the P300 component, with positive correlations with the amplitude of the P3 component for measures of attention. The spatial, temporal, and amplitude characteristics of ERP in adolescents with impaired mental development reflect the functional incompetence or immaturity of the hierarchical organization of the system processing and discriminating information arriving in the brain and requiring concentration of attention and decision-taking.

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

EEG/ERP: Within Episodic Assessment Framework for Cognition

This work explores the potential for electroencephalography (EEG)-based event-related potential (ERP) measurements to be included in the framework for episodic assessment. In current discrete clinical cognitive assessment methods used to diagnose mild cognitive impairment (MCI)/dementia, physicians see patients annually or biannually. They rely on patient’s history and clinical cognitive assessments to identify changes in memory or functional ability as indications of MCI/dementia in conjunction with exclusionary tests. They are limited in these discrete assessments by their accuracy and infrequent occurrence. The episodic assessment framework proposed will allow better ongoing information about the true well-being of the patient through assessment of memory and functional cognitive ability on a weekly or even daily basis between appointments. This paper identifies features of EEG/ERP measurements during Neuropsychological Behavioral Testing that could allow the future inclusion in the framework using now available consumer EEG devices. A pilot group of 32 participants (17 healthy, 15 MCI) was studied using a 1-back test, while their brain activity was measured using EEG. Features of the ERP thus generated were identified and measured. These ERP features and associated behavioral measurements from the 1-back test were analyzed to identify features both alone and in combination that allowed the correct classification of the participants using a repeated leave-one-out train and test method. A feature pair (1-back response time and CPz correlation) was identified that provides the best two-feature performance (1 false positive/1 false negative error) with a third feature (response required – Pz P1 to N1 latency) providing additional improvement. Given these results, it is possible that episodic ERP measurements may help with cognitive impairment diagnosis in the future.

S143 Emotions, episodic memory and error monitoring – Age-dependent event-related potential characteristics

Objectives The aim of the study was to analyze the event-related potential (ERP) features of working memory and error monitoring processes both of which are assumed to decline by aging. For this purpose, age-related changes of emotional word recognition and decision making patterns in risky conditions were investigated. Methods Data were collected in three different experimental conditions in young (18–26 yrs) and elderly (60–75 yrs) participants. In an episodic memory task subjects were asked to memorize a set of emotional words and word recognition test was performed. The two-choice-single-outcome gambling and the Balloon Analogue Risk Tasks (BART) were used to study sequential decision making risking to lose or to gain by each move. Results The old/new effect on the N400 ERP component and the effect of valence for negative words were more pronounced in the young. The feedback-related negativity component corresponded to the goodness of outcome and the P3 showed complex feedback evaluation, but only in the young. The reward positivity ERP component increased as a function of reward contingencies, but only in the young. Discussion and conclusions The findings indicate prioritized processing of aversive stimuli in the young. The neural mechanisms signaling the most salient feedback stimulus properties are altered by aging. Sequential tracking of outcome contingencies has a key role in action planning and progressive expectation. Significance The obvious decline of the cognitive functions investigated indicate diminished capacity of adaptive behavior in the elderly.

Changes in event-related potentials in patients with first-episode schizophrenia and their siblings

BackgroundThis study aimed to explore the characteristics of event-related potentials induced by facial emotion recognition in patients with first-episode schizophrenia and in their siblings.MethodsIn this case-control study, 30 first-episode schizophrenia patients, 26 siblings, and 30 healthy controls were enrolled. They completed facial emotion recognition tasks from the Ekman Standard Faces Database as an induction for evoked potentials. Evoked potential data were obtained using a 64-channel electroencephalography system. Average evoked potential waveforms were computed from epochs for each stimulus type. The amplitudes and latency of the event-related potentials for P100 (positive potential 100 ms after stimulus onset), N170 (negative potential 170 ms after stimulus onset), and N250 (fronto-central peak) were investigated at O1, O2, P7, and P8 electrode locations.ResultsThere were significant differences between the groups for P100 amplitude (F = 11.526, P < 0.001), electrode position (F = 450.592, P < 0.001), emotion (disgust vs. happiness vs. fear) (F = 1722.467, P < 0.001), and emotion intensity (low vs. moderate vs. high) (F = 1737.169, P < 0.001). Post hoc analysis showed significantly larger amplitudes in the schizophrenia group at the O1, O2, P7, and P8 electrode positions. There were no significant differences between the siblings of schizophrenia patients and the healthy controls.ConclusionsPatients with schizophrenia showed abnormalities in P100 amplitude, but similar results were not observed in their siblings. These results provide evidence of dysfunctional event-related potential patterns underlying facial emotion processing in patients with schizophrenia. P100 may be a characteristic index of schizophrenia.

Automated and ERP-Based Diagnosis of Attention-Deficit Hyperactivity Disorder in Children

Event-related potential (ERP) is one of the most informative and dynamic methods of monitoring cognitive processes, which is widely used in clinical research to deal with a variety of psychiatric and neurological disorders such as attention-deficit/hyperactivity disorder (ADHD). In this study, there were 60 participants including 30 patients with ADHD and 30 subjects as a control group. Their ERP signals were recorded by three electrodes in two modalities. After a preprocessing step, several features such as band power, fractal dimension, autoregressive (AR) model coefficients and wavelet coefficients were extracted from recorded signals. The aim of this study is to achieve a high classification rate. The results show that the fractal dimension-wavelet combination features provided a good discriminative capability; it should be noted that this improvement was achieved by combining all sets of features and applying a feature selection algorithm, which resulted in a maximum accuracy rate of 88.77 and 95.39% in support vector machine (SVM) and v_SVM classification algorithms using a 10-fold cross-validation approach, respectively. ERP has been widely used for clinical diagnosis and cognitive processing deficits in children with ADHD. To increase the accuracy of the diagnostic process of ADHD, ERP signals were recorded to extract some specific ERP features related to this disease for classifying the two groups. The results show that the Fra-wave characterization produced the best average accuracy with an efficiency of 99.43% for v_SVM classifier, compared with 97.65% efficiency for the wavelet features and the other features.

The relationship between ERP components and EEG spatial complexity in a visual Go/Nogo task.

The reaction times, the latencies/amplitudes of event-related potential (ERP) components, the Go/Nogo N2 effect, and the Go/Nogo P3 effect are linked to the electroencephalographic (EEG) spatial complexity level. The EEG spatial complexity is closely related to demands of certain cognitive processes and could reflect the neural processing efficiency of human brain. Obtaining the single-trial ERP features through single-trial spatial complexity may be a more efficient approach than traditional methods.

Accounting for time dependence in large-scale multiple testing of event-related potential data

Event-related potentials (ERPs) are recordings of electrical activity along the scalp time-locked to perceptual, motor and cognitive events. Because ERP signals are often rare and weak, relative to the large between-subject variability, establishing significant associations between ERPs and behavioral (or experimental) variables of interest poses major challenges for statistical analysis. Noting that ERP time dependence exhibits a block pattern suggesting strong local and long-range autocorrelation components, we propose a flexible factor modeling of dependence. An adaptive factor adjustment procedure is derived from a joint estimation of the signal and noise processes, given a prior knowledge of the noise-alone intervals. A simulation study is presented using known signals embedded in a real dependence structure extracted from authentic ERP measurements. The proposed procedure performs well compared with existing multiple testing procedures and is more powerful at discovering interesting ERP features.

多感覚AR-BCIの開発に向けた基礎的研究

A Brain-Computer Interface (BCI) enables users who cannot move their own body freely to manipulate machines. Recently, a system which is a combination of BCI and Augmented Reality (AR) is proposed. This system can link real machines to augmented markers and realize an intuitive interface. However, conventional AR-BCI system uses only visual stimuli, so a user cannot operate machines behind him/her. To deal with this problem, we propose a multisensory AR-BCI using three dimensional stereophonic sound, which provides choices behind users by generating sounds including position information. First, we investigate the amplitude and latency of event-related potential (ERP) induced by three dimensional stereophonic sound. The result shows the latency of ERP is longer (650 ms) than conventional BCI. This result suggests that the feature should be extracted with a longer time window. Second, we compare the accuracy of separate classification with combined one under the mixed visual and auditory stimuli. The result shows separate classification is better than combined one (p<0.05). Therefore, our research proposes separate classification method which comes from the basic features of ERPs under multisensory AR-BCI system and shows the appropriateness of proposed method.

Characterization of spatio-temporal epidural event-related potentials for mouse models of psychiatric disorders.

Distinctive features in sensory event-related potentials (ERPs) are endophenotypic biomarkers of psychiatric disorders, widely studied using electroencephalographic (EEG) methods in humans and model animals. Despite the popularity and unique significance of the mouse as a model species in basic research, existing EEG methods applicable to mice are far less powerful than those available for humans and large animals. We developed a new method for multi-channel epidural ERP characterization in behaving mice with high precision, reliability and convenience and report an application to time-domain ERP feature characterization of the Sp4 hypomorphic mouse model for schizophrenia. Compared to previous methods, our spatio-temporal ERP measurement robustly improved the resolving power of key signatures characteristic of the disease model. The high performance and low cost of this technique makes it suitable for high-throughput behavioral and pharmacological studies.

A clinical trial to validate event-related potential markers of Alzheimer's disease in outpatient settings

IntroductionWe investigated whether event-related potentials (ERP) collected in outpatient settings and analyzed with standardized methods can provide a sensitive and reliable measure of the cognitive deficits associated with early Alzheimer's disease (AD). MethodsA total of 103 subjects with probable mild AD and 101 healthy controls were recruited at seven clinical study sites. Subjects were tested using an auditory oddball ERP paradigm. ResultsSubjects with mild AD showed lower amplitude and increased latency for ERP features associated with attention, working memory, and executive function. These subjects also had decreased accuracy and longer reaction time in the target detection task associated with the ERP test. DiscussionAnalysis of ERP data showed significant changes in subjects with mild AD that are consistent with the cognitive deficits found in this population. The use of an integrated hardware/software system for data acquisition and automated data analysis methods make administration of ERP tests practical in outpatient settings.

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system.

In a previous study (unpublished), Emotiv headset was validated for capturing event-related potentials (ERPs) from normal subjects. In the present follow-up study, the signal quality of Emotiv headset was tested by the accuracy rate of discriminating Major Depressive Disorder (MDD) patients from the normal subjects. ERPs of 22 MDD patients and 15 normal subjects were induced by an auditory oddball task and the amplitude of N1, N2 and P3 of ERP components were specifically analyzed. The features of ERPs were statistically investigated. It is found that Emotiv headset is capable of discriminating the abnormal N1, N2 and P3 components in MDD patients. Relief-F algorithm was applied to all features for feature selection. The selected features were then input to a linear discriminant analysis (LDA) classifier with leave-one-out cross-validation to characterize the ERP features of MDD. 127 possible combinations out of the selected 7 ERP features were classified using LDA. The best classification accuracy was achieved to be 89.66%. These results suggest that MDD patients are identifiable from normal subjects by ERPs measured by Emotiv headset.

Motor imagery classification via combinatory decomposition of ERP and ERSP using sparse nonnegative matrix factorization

BackgroundBrain activities could be measured by devices like EEG, MEG, MRI etc. in terms of electric or magnetic signal, which could provide information from three domains, i.e., time, frequency and space. Combinatory analysis of these features could definitely help to improve the classification performance on brain activities. NMF (nonnegative matrix factorization) has been widely applied in pattern extraction tasks (e.g., face recognition, gene data analysis) which could provide physically meaningful explanation of the data. However, brain signals also take negative values, so only spectral feature has been employed in existing NMF studies for brain computer interface. In addition, sparsity is an intrinsic characteristic of electric signals. New methodTo incorporate sparsity constraint and enable analysis of time domain feature using NMF, a new solution for motor imagery classification is developed, which combinatorially analyzes the ERP (event related potential, time domain) and ERSP (event related spectral perturbation, frequency domain) features via a modified mixed alternating least square based NMF method (MALS-NMF for short). ResultsExtensive experiments have verified the effectivity the proposed method. The results also showed that imposing sparsity constraint on the coefficient matrix in ERP factorization and basis matrix in ERSP factorization could better improve the algorithm performance. Comparison with existing methodsComparisons with other eight representative methods have further verified the superiority of the proposed method. ConclusionsThe MALS-NMF method is an effective solution for motor imagery classification and has shed some new light into the field of brain dynamics pattern analysis.

P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls

ObjectiveBrain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. MethodsSixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0–25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. ResultsBCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). ConclusionsThe differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). SignificanceP300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

Classification of Familiar or Strange English Words using ERP

English vocabulary is the bricks of the language, and it’s important for English learners to handle accurately English words. Words recitation or dictation is the most widely used simple test form in vocabulary quiz. However, some students do less well in tests due to stress or even meltdown for exam. Here, we apply Brain fingerprinting (BF) in English vocabulary test. We studied the Event Related Potential (ERP) components elicited by familiar English words and strange English words, and used the ERP to decipher whether subject recognized the word presented in the screen. We analyzed the distinct ERP features related to different stimuli (familiar and strange words), and further speculated whether subjects recognized the presenting words from P300 by calculation correlations between different stimuli. The results of 8 subjects implied that familiar words recognized by subjects could be detected from P300 with accepted precision and recall. This means that BF provides a novel alternative for vocabulary quiz and opens the way for application in education, where concealed vocabulary related information stored in mind was detected by measuring brain signals, such as electroencephalography (EEG).