Cognitive Neurodynamics最新文献

Methamphetamine (MA) addiction leads to impairment of neural communication functions in the brain, and functional connectivity (FC) may be a valid indicator. However, it is unclear how FC in the brain changes in methamphetamine use disorder (MUD) after treatment with repetitive transcranial magnetic stimulation (rTMS). Thirty-four patients with MUD participated in this study. The subjects were randomized to receive the active or sham rTMS for four weeks. Subjects performed electroencephalography (EEG) examinations and visual analogue scale (VAS) assessments before and after the treatment. The FC networks were constructed and visualized, and then the graph theory analysis was carried out. Finally, machine learning was used to classify FC networks before and after rTMS. The results showed that (1) the active group showed a significant enhancement in connectivity in the beta band; (2) the global efficiency, local efficiency, and aggregation coefficient of the active group in the beta band decreased significantly; (3) the LDA algorithm combined with the beta band FC matrix achieved an average accuracy of 82.5% in distinguishing before and after treatment. This study demonstrated that brain FC could effectively assess the therapeutic effect of rTMS, among which the beta band was the most sensitive and effective frequency band.

Electroencephalogram (EEG) emotion recognition plays an important role in human–computer interaction, and a higher recognition accuracy can improve the user experience. In recent years, domain adaptive methods in transfer learning have been used to construct a general emotion recognition model to deal with domain difference among different subjects and sessions. However, it is still challenging to effectively reduce domain difference in domain adaptation. In this paper, we propose a Multiple-Source Distribution Deep Adaptive Feature Norm Network for EEG emotion recognition, which reduce domain difference by improving the transferability of task-specific features. In detail, the domain adaptive method of our model employs a three-layer network topology, inserts Adaptive Feature Norm to self-supervised adjustment between different layers, and combines a multiple-kernel selection approach to mean embedding matching. The method proposed in this paper achieves the best classification performance in the SEED and SEED-IV datasets. In SEED dataset, the average accuracy of cross-subject and cross-session experiments is 85.01 and 91.93%, respectively. In SEED-IV dataset, the average accuracy is 58.81% in cross-subject experiments and 59.51% in cross-session experiments. The experimental results demonstrate that our method can effectively reduce the domain difference and improve the emotion recognition accuracy.

Abstract

Football is one of the most played sports in the world and kicking with adequate accuracy increases the likelihood of winning a competition. Although studies with different target-directed movements underline the role of distinctive cortical activity on superior accuracy, little is known about cortical dynamics associated with kicking. Mobile electroencephalography is a popular tool to investigate cortical modulations during movement, however, inherent and artefact-related pitfalls may obscure the reliability of functional sources and their activity. The purpose of this study was therefore to describe consistent cortical dynamics underlying target-directed pass-kicks based on test–retest reliability estimates. Eleven participants performed a target-directed kicking task at two different sessions within one week. Electroencephalography was recorded using a 65-channel mobile system and behavioural data were collected including motion range, acceleration and accuracy performance. Functional sources were identified using independent component analysis and clustered in two steps with the components of first and subsequently both sessions. Reliability estimates of event-related spectral perturbations were computed pixel-wise for participants contributing with components of both sessions. The parieto-occipital and frontal clusters were reproducible for the same majority of the sample at both sessions. Their activity showed consistent alpha desyhronization and theta sychnronisation patterns with substantial reliability estimates revealing visual and attentional demands in different phases of kicking. The findings of our study reveal prominent cortical demands during the execution of a target-directed kick which may be considered in practical implementations and provide promising academic prospects in the comprehension and investigation of cortical activity associated with target-directed movements.

Working memory is a complex cognitive system that temporarily maintains purpose-relevant information during human cognition performance. Working memory performance has also been found to be sensitive to high-altitude exposure. This study used a multilevel change detection task combined with Electroencephalogram data to explore the mechanism of working memory change from high-altitude exposure. When compared with the sea-level population, the performance of the change detection task with 5 memory load levels was measured in the Han population living in high-altitude areas, using the event-related potential analysis and task-related connectivity network analysis. The topological analysis of the brain functional network showed that the normalized modularity of the high-altitude group was higher in the memory maintenance phase. Event-related Potential analysis showed that the peak latencies of P1 and N1 components of the high-altitude group were significantly shorter in the occipital region, which represents a greater attentional bias in visual early processing. Under the condition of high memory loads, the high-altitude group had a larger negative peak in N2 amplitude compared to the low-altitude group, which may imply more conscious processing in visual working memory. The above results revealed that the visual working memory change from high-altitude exposure might be derived from the attentional bias and the more conscious processing in the early processing stage of visual input, which is accompanied by the increase of the modularity of the brain functional network. This may imply that the attentional bias in the early processing stages have been influenced by the increased modularity of the functional brain networks induced by high-altitude exposure.

Abstract

We are concerned about sparsely synchronized rhythms (SSRs), associated with diverse cognitive functions, in the hippocampal dentate gyrus. Distinctly, adult-born immature GCs (imGCs) emerge through neurogenesis, in addition to the mature granule cells (mGCs) (emerged in the developmental stage). In prior work, these mGCs and imGCs were found to exhibit their distinct roles in pattern separation and integration for encoding cortical inputs, respectively. But, the underlying dynamical mechanismremains unclear. In this paper, we first study influence of the young adult-born imGCs on emergence of SSRs in the populations of the mGCs and the imGCs; population and individual firing behaviors in the SSRs are intensively studied. We then examine how the SSRs play a role in the underlying mechanism for pattern separation and integration. Particularly, quantitative relationship between SSRs of the mGCs and the imGCs and their pattern separation and integration is investigated.

In visual-imagery-based brain–computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor—uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.

Exercise induced Cognitive Function is an area needed in competitive fast ball sports that has stimulated interests of researchers due to its promising applicability in the field. It was noticed that although previous studies have suggested a role of exercise in facilitating cognitive performance, little is known regarding how to maximize these benefits. The study is undertaken to understand the effects of two types of aerobic training i.e., High Intensity Interval Exercise (HIIE) and Moderate Intensity Continuous Exercise (MCE) on executive function. For the assessment of cognition, after a four-week protocol, the Vienna Test System, a computerized assessment tool developed by Schuhfried GmbH (Moedling, Austria) is used for a defined universe of selected 20 athletes from various fast ball sports such as cricket, football, handball and volleyball. Statistical Analysis of Repeated Measured ANOVA along with post hoc test was done using SPSS version 21. Level of significance was kept at 5% with 95% study power. Collectively three variables, namely Sum of correct reactions, Sum of incorrect reactions and Sum of incorrect non-reactions; revealed improvement in attention, inhibitory function as well as executive function (p < 0.05). For fast ball athletes, the present study is suggestive of including MCE or HIIE programme in their training for 3 sessions/week; in order to optimize the improvement in cognitive level. The study can potentially guide every sports medicine team member, in order to develop an effective exercise protocol to improve the physiological as well as psychological capabilities of the athletes.

The current study used event-related potentials (ERPs) to examine the ability of task demand in modulating the effect of reward association on the processing of emotional faces. In the learning phase, a high or low reward probability was paired with male or female facial photos of angry, happy, or neutral expressions. Then, in the test phase, task demand was manipulated by asking participants to discriminate the emotionality or the gender of the pre-learned face with no reward at stake. The ERP results in the test phase revealed that the fronto-central N1 (60–100 ms) and the VPP (160–210 ms) components were sensitive to the interaction between reward and emotion, in that the differences between the mean amplitudes for high- and low-reward conditions were significantly larger in the neutral face and angry face conditions than in the happy face condition. Moreover, reward association and task demand showed a significant interaction over the right hemisphere for the N170 component (140–180 ms), with amplitude difference between high- and low-reward conditions being larger in the emotion task than that in the gender task. The later N2pc component exhibited an interaction between task demand and emotionality, in that happy faces elicited larger N2pc difference waves than angry and neutral faces did in the emotion task, but neutral faces elicited larger N2pc difference waves than angry faces did in the gender task. The N2pc effect aligned with behavioral performance. These results suggest that reward association acts as an ‘emotional tagging’ to imbue neutral or angry faces with motivational significance at early time windows. Task demand functions in a top-down way to modulate the deployment of attentional resources at the later attentional selection stage, but does not affect the early automatic processing of either emotion or reward association.

Objective

In this work we intend to design a system to classify human arousal at five levels (i.e., five stress levels) using four peripheral bio signals including photo-plethysmography measurements (PPG), galvanic skin response (GSR), thorax respiration (TR) and abdominal respiration (AR).

Method

A total of 98 young people voluntarily participated in this study, including 65 men and 33 women with an average age of 24.48 ± 4.26 years. We induced five levels of mental stress in subjects through the Stroop test. A range of physiological features from different analysis domains, including statistical, frequency, and geometrical analyzes, as well as recurrence quantification analysis (RQA) and detrended fluctuation analysis (DFA) were extracted to find out the best arousal-related features and to correlate them with arousal states. Classification of the five arousal levels is performed by a simple naïve Bayes classifier.

Results

Accuracies of 58.45%, 57.1% and 69.13% were obtained using linear features, nonlinear features and a combination of them, respectively. The combination of linear and nonlinear features resulted in the largest average accuracy of 69.13%, ICC of 88.12% and F1 of 69.43% values in the classification of five levels of mental stress.

Conclusion

This paper suggested that combining linear and nonlinear dynamic methods for the analysis of physiological data could help improve the accuracy of the recognition of arousal levels. However, it should be noted that combining multiple modalities (here, PPG, GSR and respiration modalities) by equally weighting them may not always be a good approach to improve accuracy.

Exploring the origin of beta - band oscillation in the cortex - basal ganglia model plays an important role in understanding the mechanism of Parkinson’s disease. In this paper, we investigate the effect of three synaptic transmission time delays in the subthalamic nucleus(STN) - the globus pallidus external segment(GPe) loop, the excitatory neurons in the cortex(EXN) - the inhibitory neurons in the cortex(INN) loop and EXN - STN loop on critical conditions of occurrence of beta - band oscillation through Hopf bifurcation theory including linear stability analysis, center manifold theorem and normal form analysis. Our results reveal that suitable transmission time delay can induce beta - band oscillation through Hopf bifurcation, and the critical condition under which Hopf bifurcation occurs is more sensitive to the transmission time delay in EXN - STN loop (T_3), where if (T_3 > 0.00185), beta - band oscillation always occurs for any transmission time delay in STN - GPe, EXN - INN loops. Our theoretical analyses provide some ideas for the future research of Parkinson’s disease.