Different Contexts in the Oddball Paradigm Induce Distinct Brain Networks in Generating the P300

Neurophysiological Correlates of Near-Wins in Gambling: A Systematic Literature Review

Identification of specific patterns of brain activity related to problem gambling may provide a deeper understanding of its underlying mechanisms, highlighting the importance of neurophysiological studies to better understand development and persistence of gambling behavior. The patterns of cognitive functioning have been investigated through electroencephalography (EEG) studies based on the near-win/near-miss (NW) effect. The main goal of the present study was to evaluate the neurophysiological basis of NWs and their modulation by gambling problems through a systematic review of event-related potentials (ERP) studies elicited by feedback events. The review followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA). A total of 15 studies were included, 12 comprising non-problem gamblers (NPGs) and three comparing problem gamblers (PGs) with matched controls. For the P300 component, the win outcome elicited a larger amplitude than the other outcomes (NW and loss), followed by the NW outcome, which elicited a larger amplitude than loss in some studies. For feedback-related negativity (FRN), the loss outcome evoked a more negative amplitude in several studies, despite eliciting a similar amplitude to NW outcomes in others. For PGs, the NW outcome evoked a higher amplitude of P300 than loss, while NPGs showed a similar amplitude to both outcomes. The present review gathered information from different sources and provides a consistent view of the different studies. However, studies lack systematic and robust methodologies, leading to inconsistent results and making it difficult to reach any definitive conclusions.

A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome

Patients with locked-in syndrome (LIS) and complete locked-in syndrome (CLIS) own a fully functional brain restricted within a non-functional body. In order to help LIS patients stay connected with their surroundings, brain-computer interfaces (BCIs) and related technologies have emerged. BCIs translate brain activity into actions that can be performed by external devices enabling LIS patients to communicate, leading to an increase in their quality of life. The past decade has seen the rapid development of BCIs that have the potential to be used for patients with locked-in syndrome, from which a great deal is tested only on healthy subjects and not on actual patients. This study aims to (1) provide the readers with a comprehensive study that contributes to this growing area of research by exploring the performance of BCIs tested specifically on LIS and CLIS patients, (2) give an overview of different modalities and paradigms used in different stages of the locked-in syndrome, and (3) discuss the contributions and limitations of BCIs introduced for the LIS and CLIS patients in the state-of-the-art and lay a groundwork for researchers interested in this field.

P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network

Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.

Exploring the influence of anthropomorphic appearance on usage intention on online medical service robots (OMSRs): A neurophysiological study

Online medical service robots (OMSRs) are becoming increasingly important in the medical industry, and their design has become a highly focused issue. This study investigated the neuroeconomics underlying the formation of usage intention, specifically evaluating the impact of anthropomorphic appearance and age on users' intentions to use OMSRs. Event-related potentials were used to analyze electroencephalography signals recorded from participants. This study found that OMSRs with a low anthropomorphic appearance induced larger P200 and P300 amplitudes, resulting in increased attentional resources compared to OMSRs with a moderate or high anthropomorphic appearance. OMSRs with moderate anthropomorphic appearances captured more attention and elicited larger P200 and P300 than those with high anthropomorphic appearances. Regarding age characteristics, OMSRs with senior features attracted more attention and induced larger P200 and P300 amplitudes. In terms of usage intention, compared to the others, users demonstrate a stronger usage intention towards the low anthropomorphism of OMSRs. Additionally, compared to the senior ones, users also exhibit a stronger usage intention toward a young appearance of OMSRs. These findings provide valuable insights for robot designers and practitioners to improve the appearance of OMSRs.

Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks

Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.

Discrimination of auditory verbal hallucination in schizophrenia based on EEG brain networks

Auditory verbal hallucinations (AVH) are a core positive symptom of schizophrenia and are regarded as a consequence of the functional breakdown in the related sensory process. Yet, the potential mechanism of AVH is still lacking. In the present study, we explored the difference between AVHs (n = 23) and non-AVHs (n = 19) in schizophrenia and healthy controls (n = 29) by using multidimensional electroencephalograms data during an auditory oddball task. Compared to healthy controls, both AVH and non-AVH groups showed reduced P300 amplitudes. Additionally, the results from brain networks analysis revealed that AVH patients showed reduced left frontal to posterior parietal/temporal connectivity compared to non-AVH patients. Moreover, using the fused network properties of both delta and theta bands as features for in-depth learning made it possible to identify the AVH from non-AVH patients at an accuracy of 80.95%. The left frontal-parietal/temporal networks seen in the auditory oddball paradigm might be underlying biomarkers of AVH in schizophrenia. This study demonstrated for the first time the functional breakdown of the auditory processing pathway in the AVH patients, leading to a better understanding of the atypical brain network of the AVH patients.

A Study of Event-Related Potentials During Monaural and Bilateral Hearing in Single-Sided Deaf Cochlear Implant Users

OBJECTIVES

Single-sided deafness (SSD) is characterized by a profoundly deaf ear and normal hearing in the contralateral ear. A cochlear implant (CI) is the only method to restore functional hearing in a profoundly deaf ear. In a previous study, we identified that the cortical processing of a CI signal differs from the normal-hearing ear (NHE) when directly compared using an auditory oddball paradigm consisting of pure tones. However, exactly how the brain integrates the electrical and acoustic signal is not well investigated. This study aims to understand how the provision of the CI in combination with the NHE may improve SSD CI users' ability to discriminate and evaluate auditory stimuli.

DESIGN

Electroencephalography from 10 SSD-CI participants (4 participated in the previous pure-tone study) were recorded during a semantic acoustic oddball task, where they were required to discriminate between odd and even numbers. Stimuli were presented in four hearing conditions: directly through the CI, directly to the NHE, or in free field with the CI switched on and off. We examined task-performance (response time and accuracy) and measured N1, P2, N2N4, and P3b event-related brain potentials (ERPs) linked to the detection, discrimination, and evaluation of task relevant stimuli. Sound localization and speech in noise comprehension was also examined.

RESULTS

In direct presentation, task performance was superior during NHE compared with CI (shorter and less varied reaction times [~720 versus ~842 msec], higher target accuracy [~93 versus ~70%]) and early neural responses (N1 and P2) were enhanced for NHE suggesting greater signal saliency. However, the size of N2N4 and P3b target-standard effects did not differ significantly between NHE and CI. In free field, target accuracy was similarly high with the CI (FF-On) and without the CI (FF-Off) (~95%), with some evidence of CI interference during FF-On (more variable and slightly but significantly delayed reaction times [~737 versus ~709 msec]). Early neural responses and late effects were also greater during FF-On. Performance on sound localization and speech in noise comprehension (S CI N NHE configuration only) was significantly greater during FF-On.

CONCLUSIONS

Both behavioral and neural responses in the semantic oddball task were sensitive to CI in both direct and free-field presentations. Direct conditions revealed that participants could perform the task with the CI alone, although performance was suboptimal and early neural responses were reduced when compared with the NHE. For free-field, the addition of the CI was associated with enhanced early and late neural responses, but this did not result in improved task performance. Enhanced neural responses show that the additional input from the CI is modulating relevant perceptual and cognitive processes, but the benefit of binaural hearing on behavior may not be realized in simple oddball tasks which can be adequately performed with the NHE. Future studies interested in binaural hearing should examine performance under noisy conditions and/or use spatial cues to allow headroom for the measurement of binaural benefit.

Antipsychotics-induced improvement of cool executive function in individuals living with schizophrenia

Cool executive dysfunction is a crucial feature in people living with schizophrenia which is related to cognition impairment and the severity of the clinical symptoms. Based on electroencephalogram (EEG), our current study explored the change of brain network under the cool executive tasks in individuals living with schizophrenia before and after atypical antipsychotic treatment (before_TR vs. after_TR). 21 patients with schizophrenia and 24 healthy controls completed the cool executive tasks, involving the Tower of Hanoi Task (THT) and Trail-Marking Test A-B (TMT A-B). The results of this study uncovered that the reaction time of the after_TR group was much shorter than that of the before_TR group in the TMT-A and TMT-B. And the after_TR group showed fewer error numbers in the TMT-B than those of the before_TR group. Concerning the functional network, stronger DMN-like linkages were found in the before_TR group compared to the control group. Finally, we adopted a multiple linear regression model based on the change network properties to predict the patient's PANSS change ratio. Together, the findings deepened our understanding of cool executive function in individuals living with schizophrenia and might provide physiological information to reliably predict the clinical efficacy of schizophrenia after atypical antipsychotic treatment.

Sleep Deprivation-Induced Changes in Baseline Brain Activity and Vigilant Attention Performance

Sleep deprivation (SD) negatively affects several aspects of cognitive performance, and one of the most widely-used tools to evaluate these effects is the Psychomotor Vigilance Test (PVT). The present study investigated the possibility of predicting changes induced by SD in vigilant attention performance by evaluating the baseline electroencephalographic (EEG) activity immediately preceding the PVT stimuli onset. All participants (n = 10) underwent EEG recordings during 10 min of PVT before and after a night of SD. For each participant, the root mean square (RMS) of the baseline EEG signal was evaluated for each 1 s time window, and the respective average value was computed. After SD, participants showed slower (and less accurate) performance in the PVT task. Moreover, a close relationship between the changes in the baseline activity with those in cognitive performance was identified at several electrodes (Fp2, F7, F8, P3, T6, O1, Oz, O2), with the highest predictive power at the occipital derivations. These results indicate that vigilant attention impairments induced by SD can be predicted by the pre-stimulus baseline activity changes.

A Novel Method for Constructing EEG Large-Scale Cortical Dynamical Functional Network Connectivity (dFNC): WTCS

As a kind of biological network, the brain network conduces to understanding the mystery of high-efficiency information processing in the brain, which will provide instructions to develop efficient brain-like neural networks. Large-scale dynamical functional network connectivity (dFNC) provides a more context-sensitive, dynamical, and straightforward sight at a higher network level. Nevertheless, dFNC analysis needs good enough resolution in both temporal and spatial domains, and the construction of dFNC needs to capture the time-varying correlations between two multivariate time series with unmatched spatial dimensions. Effective methods still lack. With well-developed source imaging techniques, electroencephalogram (EEG) has the potential to possess both high temporal and spatial resolutions. Therefore, we proposed to construct the EEG large-scale cortical dFNC based on brain atlas to probe the subtle dynamic activities in the brain and developed a novel method, that is, wavelet coherence-S estimator (WTCS), to assess the dynamic couplings among functional subnetworks with different spatial dimensions. The simulation study demonstrated its robustness and availability of applying to dFNC. The application in real EEG data revealed the appealing "Primary peak" and "P3-like peak" in dFNC network properties and meaningful evolutions in dFNC network topology for P300. Our study brings new insights for probing brain activities at a more dynamical and higher hierarchical level and pushing forward the development of brain-inspired artificial neural networks. The proposed WTCS not only benefits the dFNC studies but also gives a new solution to capture the time-varying couplings between the multivariate time series that is often encountered in signal processing disciplines.

Odd-even oddball task: Evaluating event-related potentials during word discrimination compared to speech-token and tone discrimination

Tonal and speech token auditory oddball tasks have been commonly used to assess auditory processing in various populations; however, tasks using non-word sounds may fail to capture the higher-level ability to interpret and discriminate stimuli based on meaning, which are critical to language comprehension. As such, this study examines how neural signals associated with discrimination and evaluation-processes (P3b) from semantic stimuli compare with those elicited by tones and speech tokens. This study comprises of two experiments, both containing thirteen adults with normal hearing in both ears (PTA ≤ 20 dB HL). Scalp electroencephalography and auditory event related potentials were recorded in free field while they completed three different oddball tasks: (1) tones, (2) speech tokens and (3) odd/even numbers. Based on the findings of experiment one, experiment two was conducted to understand if the difference in responses from the three tasks was attributable to stimulus duration or other factors. Therefore, in experiment one, stimulus duration was not controlled and in experiment two, the duration of each stimulus was modified to be the same across all three tasks (∼400 ms). In both experiments, P3b peak latency was significantly different between all three tasks. P3b amplitude was sensitive to reaction time, with tasks that had a large reaction time variability resulting in the P3b amplitude to be smeared, thereby reducing the amplitude size. The findings from this study highlight the need to consider all factors of the task before attributing any effects to any additional process, such as semantic processing and mental effort. Furthermore, it highlights the need for more cautious interpretation of P3b results in auditory oddball tasks.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Is an anthropomorphic app icon more attractive? Evidence from neuroergonomomics

Exploring what types of app icons are attractive has been a topic of great interest in recent years. The main purpose of this study was to explore the neural mechanism of attention capturing of the anthropomorphic app icons based on neuroergonomics. Participants' perception of different app icons was investigated by using event-related potentials (ERPs) and attractiveness evaluation. The results showed that anthropomorphic app icons were evaluated more attractive and elicted larger P2, P3 and LPP amplitude than non-anthropomorphic app icons, which indicated an attention bias to attractive anthropomprphic app icons. The time course of the attention towards anthropomorphic app icons includes three main processes: an early stimulus-driven perceptual detection of app icon features (P2 during 160-200 ms), an involuntary allocation of attention to evaluate and categorize app icons (P3 during 300-500 ms), and experiencing different emotions to anthropomorphic versus non-anthropomorphic app icons (LPP during 500-800 ms). That is, the process of users' perception and attention toward app icons combines "bottom-up" and "top-down" processes. Our findings suggest a new perspective to use ERP components (P2, P3, and LPP) to deep understanding of app icon design. A practical implication is that app icons could be designed using anthropomorphic elements to attract users.

Neural mechanism for dynamic distractor processing during video target detection: Insights from time-varying networks in the cerebral cortex

In dynamic video target detection tasks, distractors may suddenly appear due to the dynamicity of the visual scene and the uncertainty of the visual information, strongly influencing participants' attention and target detection performance. Moreover, the neural mechanism that accounts for dynamic distractor processing remains unknown, which makes it difficult to compensate for in EEG-based video target detection. Here, cortical activities with high spatiotemporal resolution were reconstructed using the source localization method. The time-varying networks among important brain regions in different cognitive phases, including information integration, decision-making, and execution, were identified to investigate the neural mechanism of dynamic distractor processing. The experimental results indicated that dynamic distractors could induce a P3-like component. In addition, there was obvious asymmetry between the two hemispheres during video target detection. Specifically, the brain responses induced by dynamic distractors were weak and more concentrated in the left hemisphere during the information integration phase; left superior frontal gyrus activity related to preparation for the presence of distractors was critical, while the attention network and primary visual network, especially in the left visual pathway, were more active for dynamic targets during the decision-making phase. These findings provide guidance for designing an effective EEG-based model for dynamic video target detection.

Effect of the Trigeminal Nerve Stimulation on Auditory Event-Related Potentials

Trigeminal sensorimotor activity stimulates arousal and cognitive performance, likely through activation of the locus coeruleus (LC). In this study we investigated, in normal subjects, the effects of bilateral trigeminal nerve stimulation (TNS) on the LC-dependent P300 wave, elicited by an acoustic oddball paradigm. Pupil size, a proxy of LC activity, and electroencephalographic power changes were also investigated. Before TNS/sham-TNS, pupil size did not correlate with P300 amplitude across subjects. After TNS but not sham-TNS, a positive correlation emerged between P300 amplitude and pupil size within frontal and median cortical regions. TNS also reduced P300 amplitude in several cortical areas. In both groups, before and after TNS/sham-TNS, subjects correctly indicated all the target stimuli. We propose that TNS activates LC, increasing the cortical norepinephrine release and the dependence of the P300 upon basal LC activity. Enhancing the signal-to-noise ratio of cortical neurons, norepinephrine may improve the sensory processing, allowing the subject to reach the best discriminative performance with a lower level of neural activation (i.e., a lower P300 amplitude). The study suggests that TNS could be used for improving cognitive performance in patients affected by cognitive disorders or arousal dysfunctions.

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding

Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.

An Asynchronous Hybrid Spelling Approach Based on EEG-EOG Signals for Chinese Character Input

In this paper, we presented a novel asynchronous speller for Chinese sinogram input by incorporating electroencephalography (EOG) into the conventional electroencephalography (EEG)-based spelling paradigm. An EOG-based brain switch was used to activate a classic row-column P300-based speller only when spelling was needed, enabling an asynchronous operation of the system. Then, the user could input sinograms by alternately performing P300 and double-blink tasks until he or she intended to stop spelling. With the incorporation of an EOG detector, the system achieved rapid sinogram input. In addition to asynchronous operation, the performance of the proposed speller was compared with that achieved by a P300-based method alone across 18 subjects. The proposed system showed a mean communication speed of approximately 2.39 sinograms per minute, an increase of 0.83 sinograms per minute compared with the P300-based method. The preliminary online performance indicated that the proposed paradigm is a very promising approach for practical Chinese sinogram input application. This system may also be expanded to users whose languages are written in logographic scripts to serve as an assistive communication tool.