ERP Features Research Articles

Non-classical event-related potentials reveal attention network alteration in patients with temporal lobe epilepsy

ObjectiveTo explore the characteristic changes of multiple ERP components associated with attention impairments in patients with temporal lobe epilepsy (TLE). MethodsA total of 92 patients diagnosed with TLE at Xiangya Hospital during May 2022 and January 2023 and 85 healthy controls were included in this study. Participants were asked to complete attention network test with recording of electroencephalogram. ResultsCompared with healthy controls, significant lower amplitudes (cue-related N1, N2 and CNV) and longer latencies (target-related N2) were found in TLE patients. Besides classical components, other components could also reveal the impairments of attention function. Cue-related N1 (p ≤ 0.007) and N2 (p ≤ 0.01) components indicated impaired alerting and orienting network in TLE. And cue-related CNV-E component (p = 0.003) promoted the alerting network was damaged and target-related N2 component (p = 0.008) indicated the executive control network was impaired. ConclusionThese findings consummate the non-classical ERP features of attention impairments in TLE patients. SignificanceThe above findings have strong clinical guiding significance for early identification and intervention.

Effects of acute stress on biological motion perception.

Biological motion perception is an essential part of the cognitive process. Stress can affect the cognitive process. The present study explored the intrinsic ERP features of the effects of acute psychological stress on biological motion perception. The results contributed scientific evidence for the adaptive behavior changes under acute stress. After a mental arithmetic task was used to induce stress, the paradigm of point-light displays was used to evaluate biological motion perception. Longer reaction time and lower accuracy were found in the inverted walking condition than in the upright walking condition, which was called the "inversion effect". The P2 peak amplitude and the LPP mean amplitude were significantly higher in the local inverted perception than in the local upright walking condition. Compared to the control condition, the stress condition induced lower RT, shorter P1 peak latency of biological motion perception, lower P2 peak amplitude and LPP mean amplitude, and higher N330 peak amplitude. There was an "inversion effect" in biological motion perception. This effect was related to the structural characteristics of biological motion perception but unrelated to the state of acute psychological stress. Acute psychological stress accelerated the reaction time and enhanced attention control of biological motion perception. Attention resources were used earlier, and less attentional investment was made in the early stage of biological motion perception processing. In the late stage, a continuous weakening of inhibition was shown in the parieto-occipital area.

Effect of Acute Psychological Stress on Speed Perception: An Event-Related Potential Study

The present study tested the intrinsic ERP features of the effects of acute psychological stress on speed perception. A mental arithmetic task was used to induce acute psychological stress, and the light spot task was used to evaluate speed perception. Compared with judgments in the constant speed and uniform acceleration motion, judgments in the uniform deceleration motion were made more quickly and with higher accuracy; attention control was higher and peaked later; and there was longer N2 peak latency, larger N2 peak amplitude, and lower mean amplitude of the late negative slow wave (SW). Under stress, the reaction time was significantly shorter. The N2 peak amplitude and SW mean amplitude were significantly higher, attention control was higher and appeared earlier, and there was a greater investment of cognitive resources. The type of movement and evoked stress also interacted to predict behavioral and ERP measures. Under acute stress, judgments made in the uniform deceleration motion condition elicited lower N2 peak latency, higher attention control, and later peak attention. The results suggest that judgments of the speed of decelerating motion require a lower investment of cognitive resources than judgments of other kinds of motion, especially under acute stress. These findings are best interpreted in terms of the interaction of arousal and attention.

Demonstration Comparison of the Features for ERP, SCM and CRM

With the further development of the digital economy in the world, MIS play a more and more significant role in the process of economic activities in various enterprises. Contemporarily, a variety of systems are emerging, causing certain troubles for the digital transformation of the enterprise. Comparing the features of different systems is an important guide for the digital transformation of enterprises. This paper will describe and compare the features of ERP, SCM, and CRM using a case study approach based on previous data. This paper firstly introduces the basic knowledge, including definitions and history of MIS. Subsequently, definitions, requirements expected effects, advantages and disadvantages of ERP, SCM and CRM are demonstrated respectively with specific successful cases. Finally, an outlook on the development of MIS is proposed and the limitations are clarified for the state-of-art systems. These results shed light on guiding further exploration of various MIS and the digital transformation for enterprises.

Fusion of pain avoidance and the contingent negative variation induced by punitive condition predict suicide ideation in a college population

This study examined behavioral and ERP features involved in pain processing as predictors of suicide ideation. Twenty-seven depressed undergraduates with high suicide ideation (HSI), 23 depressed undergraduates with low suicide ideation (LSI), and 32 healthy controls (HCs) completed the clinical Scales. The amplitudes of LPP, P2, P3, CNV, FRN, power in the beta, theta, and delta bands in the SAID task were multimodal EEG features. A machine learning algorithm known as support vector machine was used to select optimal feature sets for predicting pain avoidance, depression, and suicide ideation. The accuracy of suicide ideation classification was significantly higher for multimodal features (78.16%) which pain avoidance ranked the first and the CNV ranked the fifth than a single ERP feature model (66.62%). Pain avoidance emerged as the most optimal feature of suicide ideation classification than depression. And the CNV elicited by punitive cues may be a biomarker in suicide ideation. Pain avoidance and its related EEG components may improve the efficacy of suicide ideation classification as compared to depression.

The Erp Feature Of Baduanjin Preparatory Posture In Different Attention Condition

The Erp Feature Of Baduanjin Preparatory Posture In Different Attention Condition

Fused or Unfused? The Parable of ERP II

One of the major visions is having one system that covers all business functions and satisfies virtually all the standard processes and routine transactions within organizations. In the last decade, several academics and practitioners have predicted the rise of what is called enterprise resource planning systems II (ERP II). ERP II was sought to be a digital platform that is capable of supporting timely decision-making through covering all business functions’ processes through having preloaded modules that will minimize the need for external systems like separate customer relationship management (CRM), e-business platforms, and supply chain management (SCM) systems, among others. While ERP systems nowadays have matured, and several packages come with CRM modules and other solutions, however separate CRM systems are still widely adopted by organizations. Thus, this study investigates why organizations that currently have ERPs with CRM modules are still investing in separate CRM systems. Our results show that the current ERP systems did not reach the ERP II state as envisioned, as most organizations are inclined to adopt separate CRM systems. Thus, we have presented five main reasons for this inclination, which are: scoping during ERP implementations, costs, features and functionalities, user-friendliness and ease of use, and finally integration with e-business platforms.

Using Muse: Rapid Mobile Assessment of Brain Performance.

The advent of mobile electroencephalography (mEEG) has created a means for large scale collection of neural data thus affording a deeper insight into cognitive phenomena such as cognitive fatigue. Cognitive fatigue – a neural state that is associated with an increased incidence of errorful performance – is responsible for accidents on a daily basis which at times can cost human lives. To gain better insight into the neural signature of cognitive fatigue in the present study we used mEEG to examine the relationship between perceived cognitive fatigue and human-event related brain potentials (ERPs) and electroencephalographic (EEG) oscillations in a sample of 1,000 people. As a secondary goal, we wanted to further demonstrate the capability of mEEG to accurately measure ERP and EEG data. To accomplish these goals, participants performed a standard visual oddball task on an Apple iPad while EEG data were recorded from a Muse EEG headband. Counter to traditional EEG studies, experimental setup and data collection was completed in less than seven minutes on average. An analysis of our EEG data revealed robust N200 and P300 ERP components and neural oscillations in the delta, theta, alpha, and beta bands. In line with previous findings we observed correlations between ERP components and EEG power and perceived cognitive fatigue. Further, we demonstrate here that a linear combination of ERP and EEG features is a significantly better predictor of perceived cognitive fatigue than any ERP or EEG feature on its own. In sum, our results provide validation of mEEG as a viable tool for research and provide further insight into the impact of cognitive fatigue on the human brain.

Psychological Test System and Signal Processing Algorithm for Intelligent Wheelchair

Intelligent wheelchair based on BCI is a hot spot in recent research, and brain activity is actually a reflection of psychological activity. Completing the design of psychological test system is the key to BCI. ERP reflects the psychological activity of the brain, is a psychological test good tools such as cognitive neuroscience research. And there is psychological test system is mostly based on the traditional theory of external behavior inference, unable to objectively evaluate people’s psychological activity. In the study of psychological tests and event related potential theory, the article improved FastICA algorithm, and applied to feature extraction of ERP. Eventually set up a complete set of psychological test system. Experiments show that the system can effectively detect the ERP feature extraction, and faster, better than other conventional psychological test system. So as to lay the foundation for the completion of the design of Intelligent wheelchair.

Is it possible to implement ERP in the production function of civil construction?

Abstract: This article aims to identify the critical factors of success for the implementation of an ERP system in the production function of civil construction. The results were obtained from a research of exploratory type, with a qualitative approach, having the multiple-case study as a research strategy and as a method of data collection an interview and thematic coding as an analysis tool. The research identified that the usage of ERP in the production function of construction companies is feasible and it brought as a new contribution to the subject the conclusion that the implementation success is much more related to the organizational policies (planning culture, strategic guideline and production management model) than the ERP features. This research also endorsed the critical factors of success already pointed out by the academia, such as the supplier’s profile, the support from the senior management, and training. Regarding the training, it is highlighted the necessity to change the engineer profile.

Impact of Ethnicity on the Prevalence of Early Repolarization Pattern in Children: Comparison Between Caucasian and African Populations

The patterns and prevalence of early repolarization pattern (ER) in pediatric populations from ethnic backgrounds other than Caucasian have not been determined. Black African children (ages 4-12) from north-west Madagascar were prospectively recruited and their ECGs compared with those of age- and sex-matched Caucasian ethnicity individuals. ER was defined by ≥ 0.1mV J-point elevation in at least two contiguous inferior and/or lateral ECG leads. A total of 616 children were included. There was a trend toward a higher frequency of ER in the Africans compared to the Caucasians (23.3% vs. 17.1%, respectively, p = 0.053). The subtype (slurred vs. notched) and location of ER (lateral, inferior, or inferior-lateral) were significantly different in the two groups (p < 0.001 and p = 0.020, respectively). There was no significant difference in the number of high-risk ECG features of ERP (i.e., horizontal/descendent pattern, inferior or inferior-lateral location or J-waves ≥ 2mm) between African and Caucasian children. On the multivariate analysis, African ethnicity was an independent predictive factor of ER (OR 3.57, 95% CI 2.04-6.25, p < 0.001). African children have an increased risk of ER compared to Caucasian counterparts. Future studies should clarify the clinical and prognostic significance of ER in the pediatric population, and whether ethnicity has an impact on the outcomes.

Multimodal detection of concealed information using Genetic-SVM classifier with strict validation structure

Abstract The main idea of multimodal detection of concealed information is based on a comparison between the physiological responses of a subject to the probe and irrelevant items. The purpose of the present study was to extend the previous study by using a new machine learning structure to discriminate guilty and innocent subjects. The new machine learning structure was designed based on utilizing genetic support vector machine (GSVM) as a classifier. Evaluation of GSVM was performed through a strict validation structure to prevent from optimistic result. But this method entailed the problem of finding several optimal parameters in one optimization problem. To solve this problem, an innovative approach based on “the most frequent result” was introduced, and therefore the general optimal parameters in each feature subset were obtained. The classification accuracy of GSVM based on ERP feature was approximately 90%. The best accuracy among autonomic features and their combinations was around 95.45% and among combinations of ERP and autonomic features was around 93%. In comparison with previously used methods on the same data, GSVM had higher accuracy in all of the feature subsets. The new presented machine learning structure made experimental conditions closer to real conditions by using strict validation and introducing general optimal parameter. On the other hand, the higher accuracy of GSVM shows the better SVM's ability to find complex patterns and solve classification problems as compared with the other methods. Moreover, the crucial role of Genetic algorithms to find the optimal parameters shouldn't be ignored.

ERP II is Dead- Long Live CRM

In the early 2000s, several practitioners and authors have predicted the overshadowing of customer relationship management (CRM) systems by what is called enterprise resource planning systems II (ERP II). ERP II was envisioned to virtually cover all the processes within organizations from supply chain, CRM, to e-business. In 2000, Gartner group published an article titled “ERP is dead, long live ERP” in which they argued that in near future, ERP systems will overcome the need for other specialized systems (e.g. CRM). While there have been major advances in ERP systems since then, and the fact that most current ERP systems include CRM modules and applications, however, separate CRM systems are still widely adopted by organizations. Thus, this research attempts to investigate the main reasons that motivate organizations to invest in distinct CRM systems instead of using the CRM related modules in the acquired ERP system. Via a multiple case study, our results show that ERP II as envisioned, is not existent yet. Organizations tend to prefer separate CRM systems. Thus, we have developed four main reasons for this preference, which are: scoping during ERP implementations, costs, features, user-friendliness and ease of use. This paper, ultimately, leaves space for further research.

Training and testing ERP-BCIs under different mental workload conditions

Objective. As one of the most popular and extensively studied paradigms of brain–computer interfaces (BCIs), event-related potential-based BCIs (ERP-BCIs) are usually built and tested in ideal laboratory settings in most existing studies, with subjects concentrating on stimuli and intentionally avoiding possible distractors. This study is aimed at examining the effect of simultaneous mental activities on ERP-BCIs by manipulating various levels of mental workload during the training and/or testing of an ERP-BCI. Approach. Mental workload was manipulated during the training or testing of a row–column P300-speller to investigate how and to what extent the spelling performance and the ERPs evoked by the oddball stimuli are affected by simultaneous mental workload. Main results. Responses of certain ERP components, temporal–occipital N200 and the late reorienting negativity evoked by the oddball stimuli and the classifiability of ERP features between targets and non-targets decreased with the increase of mental workload encountered by the subject. However, the effect of mental workload on the performance of ERP-BCI was not always negative but depended on the conditions where the ERP-BCI was built and applied. The performance of ERP-BCI built under an ideal lab setting without any irrelevant mental activities declined with the increasing mental workload of the testing data. However, the performance was significantly improved when an ERP-BCI was built under an appropriate mental workload level, compared to that built under speller-only conditions. Significance. The adverse effect of concurrent mental activities may present a challenge for ERP-BCIs trained in ideal lab settings but which are to be used in daily work, especially when users are performing demanding mental processing. On the other hand, the positive effects of the mental workload of the training data suggest that introducing appropriate mental workload during training ERP-BCIs is of potential benefit to the performance in practical applications.

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations.

A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time-frequency points about 5.3Hz and 0.3s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.

ERP Feature of Matching Pennies

Game process is a complex process, the process of the game is reflected in the subject's brain waves will be in matching pannies out of the coin, so to analyze brain waves of people can study the game process, ERP is an important means of EEG studies, through the course of the game the game ERP analysis process characteristics.

Development of auditory event‐related potentials in infants prenatally exposed to methadone

Developmental features of the P2 auditory ERP in a change detection paradigm were examined in infants prenatally exposed to methadone. Opiate dependent pregnant women maintained on methadone replacement therapy were recruited during pregnancy (N = 60). Current and historical alcohol and substance use, SES, and psychiatric status were assessed with a maternal interview during the third trimester. Medical records were used to collect information regarding maternal medications, monthly urinalysis, and breathalyzer to confirm comorbid drug and alcohol exposures. Between birth and 4 months infant ERP change detection performance was evaluated on one occasion with the oddball paradigm (.2 probability oddball) using pure-tone stimuli (standard = 1 kHz and oddball = 2 kHz frequency) at midline electrode sites, Fz, Cz, Pz. Infant groups were examined in the following developmental windows: 4-15, 16-32, or 33-120 days PNA. Older groups showed increased P2 amplitude at Fz and effective change detection performance at P2 not seen in the newborn group. Developmental maturation of amplitude and stimulus discrimination for P2 has been reported in developing infants at all of the ages tested and data reported here in the older infants are consistent with typical development. However, it has been previously reported that the P2 amplitude difference is detectable in neonates; therefore, absence of a difference in P2 amplitude between stimuli in the 4-15 days group may represent impaired ERP performance by neonatal abstinence syndrome or prenatal methadone exposure.

Electrocortical Features of Depression and Their Clinical Utility in Assessing Antidepressant Treatment Outcome

Major depressive disorder (MDD) is primarily characterized by decreased affect and accompanying behavioural consequences, but it is also associated with cognitive dysfunction. Assessment of electroencephalographic (EEG) activity and associated event-related potentials (ERPs; derived from averaged EEG activity in response to a stimulus) in the context of MDD has provided insights into the electrocortical abnormalities associated with the disorder. Importantly, EEG and ERPs also have emerged as candidates for predicting and optimizing antidepressant (AD) treatment outcome. This is critical in light of relatively low remission rates or a limited response to initial AD interventions. In contrast to other neuroimaging approaches, EEG and ERPs may be superior for predicting and monitoring AD response, as electrocortical measures are relatively inexpensive, easy to use, and have excellent temporal (that is, millisecond) resolution, enabling fine-grained assessment of basic cognitive and emotive processes. This review aims to highlight the most consistently noted EEG and ERP features in MDD, which may one day assist with diagnostic confirmation, as well as the potential clinical utility of specific electrocortical measures in aiding with response prediction.

Single trial ERP reading based on parallel factor analysis

The extraction of task-related single trial ERP features has recently gained much interest, in particular in simultaneous EEG-fMRI applications. In this study, a specific decomposition known as parallel factor analysis (PARAFAC) was used, in order to retrieve the task-related activity from the raw signals. Using visual detection task data, acquired in normal circumstances and simultaneously with fMRI, differences between distinct task-related conditions can be captured in the trial signatures of specific PARAFAC components when applied to ERP data arranged in Channels × Time × Trials arrays, but the signatures did not correlate with the fMRI data. Despite the need for parameter tuning and careful preprocessing, the approach is shown to be successful, especially when prior knowledge about the expected ERPs is incorporated.

Empirically Validating Fully Automated EOG Artifact Correction using Independent Components Analysis

Empirically Validating Fully Automated EOG Artifact Correction using Independent Components Analysis