Predicting individual decision-making responses based on the functional connectivity of resting-state EEG

Si, Yajing; Jiang, Lin; Tao, Qin; Chen, Chunli; Li, Fali; Jiang, Yuanling; Zhang, Tao; Cao, Xianyu; Wan, Feng; Yao, Dezhong; Xu, Peng

doi:10.1088/1741-2552/ab39ce

Cited by 45 publications

(37 citation statements)

References 74 publications

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“…The human brain is a complex network comprising a large number of interconnected brain regions, and neural impulses are always transmitted and integrated across the spatially separated (but functionally connected) brain regions [44]. Many studies have revealed that the resting‐state brain network is closely related to human behavior and disease progression [45 –48]. Accordingly, the organizational structure of the resting‐state brain network may play an important role in SMR modulation.…”

Section: Causes Of Mi‐bci Inefficiencymentioning

confidence: 99%

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Zhang

et al. 2020

Brain Science Advances

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Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics control, neurorehabilitation, and gaming; however, currently, most such systems only operate in controlled laboratory environments. One of the most important obstacles is the MI‐BCI inefficiency phenomenon. The accuracy of MI‐BCI control varies significantly (from chance level to 100% accuracy) across subjects due to the not easily induced and unstable MI‐related EEG features. An MI‐BCI inefficient subject is defined as a subject who cannot achieve greater than 70% accuracy after sufficient training time, and multiple survey results indicate that inefficient subjects account for 10%–50% of the experimental population. The widespread use of MI‐BCI has been seriously limited due to these large percentages of inefficient subjects. In this review, we summarize recent findings of the cause of MI‐BCI inefficiency from resting‐state brain function, task‐related brain activity, brain structure, and psychological perspectives. These factors help understand the reasons for inter‐subject MI‐BCI control performance variability, and it can be concluded that the lower resting‐state sensorimotor rhythm (SMR) is the key factor in MI‐BCI inefficiency, which has been confirmed by multiple independent laboratories. We then propose to divide MI‐BCI inefficient subjects into three categories according to the resting‐state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem. The potential solutions include developing transfer learning algorithms, new experimental paradigms, mindfulness meditation practice, novel training strategies, and identifying new motor imagery‐related EEG features. To date, few studies have focused on improving the control accuracy of MI‐BCI inefficient subjects; thus, we appeal to the BCI community to focus more on this research area. Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI‐BCI.

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Section: Causes Of Mi‐bci Inefficiencymentioning

confidence: 99%

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Zhang

et al. 2020

Brain Science Advances

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“…Figure 4 further displays the atypical interactions underlying the task information processing for ADHD in which long-range connectivity between the frontal and occipital lobes could be found. In fact, during the visual task, the occipital lobe is responsible for receiving and integrating visual information, which was revealed by Li et al's study on constructing large-scale cortical networks for P3 [ 59 ], while the frontal lobe contributes to a wide range of cognitive functions, such as attention [ 60 ], decision-making [ 61 ], and executive control [ 62 ]. The frequency-specified synchronization between these regions can effectively modulate cognitive information processing [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

Altered Functional Connectivity in Children with ADHD Revealed by Scalp EEG: An ERP Study

Chen

Yang

et al. 2021

Neural Plasticity

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Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental brain disorders in childhood. Despite extensive researches, the neurobiological mechanism underlying ADHD is still left unveiled. Since the deficit functions, such as attention, have been demonstrated in ADHD, in our present study, based on the oddball P3 task, the corresponding electroencephalogram (EEG) of both healthy controls (HCs) and ADHD children was first collected. And we then not only focused on the event-related potential (ERP) evoked during tasks but also investigated related brain networks. Although an insignificant difference in behavior was found between the HCs and ADHD children, significant electrophysiological differences were found in both ERPs and brain networks. In detail, the dysfunctional attention occurred during the early stage of the designed task; as compared to HCs, the reduced P2 and N2 amplitudes in ADHD children were found, and the atypical information interaction might further underpin such a deficit. On the one hand, when investigating the cortical activity, HCs recruited much stronger brain activity mainly in the temporal and frontal regions, compared to ADHD children; on the other hand, the brain network showed atypical enhanced long-range connectivity between the frontal and occipital lobes but attenuated connectivity among frontal, parietal, and temporal lobes in ADHD children. We hope that the findings in this study may be instructive for the understanding of cognitive processing in children with ADHD.

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“…Last, in this study, candidate EEG predictors were restricted only to the EEG band powers. If more EEG electrodes are available, EEG network measures can also be used to predict the dynamic ranges of EEG features [42,43], which we would like to pursue in our future study.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Cha

Han

2020

Sensors

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With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

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Predicting individual decision-making responses based on the functional connectivity of resting-state EEG

Cited by 45 publications

References 74 publications

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Altered Functional Connectivity in Children with ADHD Revealed by Scalp EEG: An ERP Study

Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

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