Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects ([Formula: see text]) or trials ([Formula: see text]). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects' EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.

The Effects of a Virtual Reality Rehabilitation Task on Elderly Subjects: An Experimental Study Using Multimodal Data

Ageing populations are becoming a global issue. Against this background, the assessment and treatment of geriatric conditions have become increasingly important. This study draws on the multisensory integration of virtual reality (VR) devices in the field of rehabilitation to assess brain function in young and old people. The study is based on multimodal data generated by combining high temporal resolution electroencephalogram (EEG) and subjective scales and behavioural indicators reflecting motor abilities. The phase locking value (PLV) was chosen as an indicator of functional connectivity (FC), and six brain regions, namely LPFC, RPFC, LOL, ROL, LMC and RMC, were analysed. The results showed a significant difference in the alpha band on comparing the resting and task states in the younger group. A significant difference between the two states in the alpha and beta bands was observed when comparing task states in the younger and older groups. Meanwhile, this study affirms that advancing age significantly affects human locomotor performance and also has a correlation with cognitive level. The study proposes a novel accurate and valid assessment method that offers new possibilities for assessing and rehabilitating geriatric diseases. Thus, this method has the potential to contribute to the field of rehabilitation medicine.

Big data analysis and prediction method of multidimensional space-time features of energy internet based on fuzzy rough model

Nowadays, energy has become a hot issue of concern to the whole society. With the unbalanced distribution of resources in the world and more severe climate change, the constraints of resource conditions and environmental status on global energy development are becoming stronger and stronger. The rapid development of the Internet, as well as the proposal of the energy Internet, has a better application in the analysis of energy demand, which can effectively alleviate the contradiction between energy and environment. Aiming at the big data of energy Internet and based on the advantages of fuzzy rough model, this paper studies a method of big data analysis and prediction of multidimensional space-time characteristics of energy Internet based on fuzzy rough model. Firstly, according to the spatio-temporal characteristics of energy Internet data, extract the multidimensional spatio-temporal characteristics of energy internet. Secondly, rough set and fuzzy set are two commonly used mathematical tools, and the combination of the two fuzzy rough models can more fully mine data information. In view of the shortcomings of the commonly used fuzzy rough set reduction algorithm, a reduction algorithm based on conditional entropy is proposed. Finally, taking multidimensional space-time characteristics as input, combining the advantages of fuzzy rough model and neural network, a prediction model is established to analyze and forecast energy demand. The simulation experiments show that the design method is feasible and superior, and can achieve the prediction of energy demand well, so as to make more rational use of energy.

The Psychophysiology of Action: A Multidisciplinary Endeavor for Integrating Action and Cognition

There is a vast amount of literature concerning the integration of action and cognition. Although this broad research area is of great interest for many disciplines like sports, psychology and cognitive neuroscience, only a few attempts tried to bring together different perspectives so far. Our goal is to provide a perspective to spark a debate across theoretical borders and integration of different disciplines via psychophysiology. In order to boost advances in this research field it is not only necessary to become aware of the different areas that are relevant but also to consider methodological aspects and challenges. We briefly describe the most relevant theoretical accounts to the question of how internal and external information processes or factors interact and, based on this, argue that research programs should consider the three dimensions: (a) dynamics of movements; (b) multivariate measures and; (c) dynamic statistical parameters. Only with an extended perspective on theoretical and methodological accounts, one would be able to integrate the dynamics of actions into theoretical advances.

Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis

BACKGROUND

Neural activity under cognitive reappraisal can be more accurately investigated using simultaneous EEG- (electroencephalography) fMRI (functional magnetic resonance imaging) than using EEG or fMRI only. Complementary spatiotemporal information can be found from simultaneous EEG-fMRI data to study brain function.

METHOD

An effective EEG-fMRI fusion framework is proposed in this work. EEG-fMRI data is simultaneously sampled on fifteen visually stimulated healthy adult participants. Net-station toolbox and empirical mode decomposition are employed for EEG denoising. Sparse spectral clustering is used to construct fMRI masks that are used to constrain fMRI activated regions. A kernel-based canonical correlation analysis is utilized to fuse nonlinear EEG-fMRI data.

RESULTS

The experimental results show a distinct late positive potential (LPP, latency 200-700ms) from the correlated EEG components that are reconstructed from nonlinear EEG-fMRI data. Peak value of LPP under reappraisal state is smaller than that under negative state, however, larger than that under neutral state. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. Meanwhile, in these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state.

CONCLUSIONS

The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is also suitable for other neuroimaging technologies using simultaneous EEG-fMRI data.

QEEG-based neural correlates of decision making in a well-trained eight year-old chess player

The neurocognitive substrates of decision making (DM) in the context of chess has appealed to researchers' interest for decades. Expert and beginner chess players are hypothesized to employ different brain functional networks when involved in episodes of critical DM upon chess. Cognitive capacities including, but not restricted to pattern recognition, visuospatial search, reasoning, planning and DM are perhaps the key determinants of rewarding and judgmental decisions in chess. Meanwhile, the precise neural correlates of DM in this context has largely remained elusive. The quantitative electroencephalography (QEEG) is an investigation tool possessing a proper temporal resolution in the study of neural correlates of cognitive tasks at cortical level. Here, we used a 22-channel EEG setup and digital polygraphy in a well-trained 8 year-old boy while engaged in playing chess against the computer. Quantitative analyses were done to map and source-localize the EEG signals. Our analyses indicated a lower power spectral density (PSD) for higher frequency bands in the right hemisphere upon DM-related epochs. Moreover, the information flow upon DM blocks in this particular case was more of posterior towards anterior brain regions.

A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition

A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.