Human Brain Activity Research Articles

A brain-like cognitive process with shared methods

This paper describes a new entropy-style of equation that may be useful in a general sense, but can be applied to a cognitive model with related processes. The model is based on the human brain, with automatic and distributed pattern activity. Methods for carrying out the different processes are suggested. The main purpose of this paper is to reaffirm earlier research on different knowledge-based and experience-based clustering techniques. The overall architecture has stayed essentially the same and so it is the localised processes or smaller details that have been updated. For example, a counting mechanism is used slightly differently, to measure a level of 'cohesion' instead of a 'correct' classification, over pattern instances. The introduction of features has further enhanced the architecture and the new entropy-style equation is proposed. While an earlier paper defined three levels of functional requirement, this paper re-defines the levels in a more human vernacular, with higher-level goals described in terms of action-result pairs.

Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm

Electroencephalography (EEG) captures the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imager (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG trial is regenerated using narrowband signals obtained from individual channel. Each channel of EEG trial is decomposed into a set of subband signals using multivariate discrete wavelet transform. The selected subbands are organized in two different ways namely vertical arrangement of subbands (VaS) and horizontal arrangement of subbands (HaS) to regenerate the trials. The features are extracted from each of the arrangements using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. The effectiveness of two classifiers– linear discriminant analysis (LDA) and support vector machine (SVM) are studied. The performances of the proposed methods are evaluated using publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

Neutro-Intelligent Set is a particular case of the Refined Neutrosophic Set

In this short note we show that the newly introduced concept of Neutro-Intelligent Set (NIS) deserves attention in its applications to the human brain activity, and that NIS is a particular case of the Refined Neutrosophic Set.

Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning

Reconstructing perceived stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant task in brain decoding. However, the inconsistent distribution and representation between fMRI signals and visual images cause great ‘domain gap’. Moreover, the limited fMRI data instances generally suffer from the issues of low signal noise ratio (SNR), extremely high dimensionality, and limited spatial resolution. Existing methods are often affected by these issues so that a satisfactory reconstruction is still an open problem. In this paper, we show that it is possible to obtain a promising solution by learning visually-guided latent cognitive representations from the fMRI signals, and inversely decoding them to the image stimuli. The resulting framework is called Dual-Variational Autoencoder/ Generative Adversarial Network (D-Vae/Gan), which combines the advantages of adversarial representation learning with knowledge distillation. In addition, we introduce a novel three-stage learning strategy which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process. Extensive experimental results on both artificial and natural images have demonstrated that our method could achieve surprisingly good results and outperform the available alternatives.

Neurophysiological Characteristics of Competition in Skills and Cooperation in Creativity Task Performance: A Review of Hyperscanning Research

The review considers the studies of neurophysiological indices of human brain activity in different types of joint actions. The results of studies were summarized to describe the neurophysiological correlates of joint performance of creative or artistic (musical) tasks by several subjects simultaneously in conditions of cooperation or competition. Involvement in joint activities presumably causes the neurophysiological rearrangements of human brain activity that are not observed during tasks performed individually, but characterize the situations of interactions between subjects. The nature of interactions (competition or cooperation) essentially affects brain activity. Interbrain synchronization has been found to increase during cooperation and decrease in conditions that do not require interactions. In performing arts (music improvisation), the performers’ coordinated activity, which can be considered as cooperation, evokes a dynamic interbrain synchronization pattern in frontal and central cortical areas mainly in low-frequency (Δ and θ) EEG bands. Functional near-infrared spectroscopy (fNIRS) has shown that cooperation in performing creativity tasks is characterized by an increase of inter-subject synchronization of the prefrontal, temporal, and temporal-parietal areas of the left and right hemispheres, while no inter-subject synchronization has been observed in competitive contexts.

A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study.

Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.

Neuromarketing v modernej spoločnosti

Neuromarketing, we can classify as an avant­garde scientifc discipline. It defnes the boundary between neuroscience and marketing. It works on marketing professionals to re­evaluate their views on consumer thinking and think about their business activities' impact. The neuromarketing aims to apply knowledge from the medical sciences about the human brain's activity and its use in the effectiveness of business strategies and other related felds of study. To offer products and services so that the buyer's behavior is directing towards the purchase. We often think that we know why we act one way or another; these are just illusions that we unknowingly create to explain our actions. Neuromarketing is a scientifc discipline that reveals the mystery of the human mind, answering why consumers prefer a given product. Neuromarketing is one step ahead of other research methods. Thanks to the fact that it can analyze the consumer's wishes, it also provides more accurate results. In our presented review study, we critically evaluate selected scientifc papers in the given area to point out the direction, perspective of neuromarketing, and other challenges and attitudes we face in today's professional environment.

Whom to Believe? Understanding and Modeling Brain Activity in Source Credibility Evaluation.

Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Source credibility is among the most important aspects of credibility evaluations. One of the most direct ways to understand source credibility is to use measurements of brain activity of humans performing credibility evaluations. Nevertheless, source credibility has never been investigated using such a method before. This article reports the results of an experiment during which we have measured brain activity during source credibility evaluation, using EEG. The experiment allowed for identification of brain areas that were active when a participant made positive or negative source credibility evaluations. Based on experimental data, we modeled and predicted human source credibility evaluations using EEG brain activity measurements with F1 score exceeding 0.7 (using 10-fold cross-validation).

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis.

BackgroundNeuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations.ObjectiveThe focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements.MethodsIn this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords.ResultsOut of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual’s mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored.ConclusionThis systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

3D Contrast Image Reconstruction From Human Brain Activity.

Several studies demonstrated that functional magnetic resonance imaging (fMRI) signals in early visual cortex can be used to reconstruct 2-dimensional (2D) visual contents. However, it remains unknown how to reconstruct 3-dimensional (3D) visual stimuli from fMRI signals in visual cortex. 3D visual stimuli contain 2D visual features and depth information. Moreover, binocular disparity is an important cue for depth perception. Thus, it is more challenging to reconstruct 3D visual stimuli than 2D visual stimuli from the fMRI signals of visual cortex. This study aimed to reconstruct 3D visual images by constructing three decoding models: contrast-decoding, disparity-decoding and contrast-disparity-decoding models, and testing these models with fMRI data from humans viewing 3D contrast images. The results revealed that the 3D contrast stimuli can be reconstructed from the visual cortex. And the early visual regions (V1, V2) showed predominant advantages in reconstructing the contrast in 3D images for the contrast-decoding model. The dorsal visual regions (V3A, V7 and MT) showed predominant advantages in decoding the disparity in 3D images for the disparity-decoding model. The combination of the early and dorsal visual regions showed predominant advantages in decoding both the contrast and disparity for the contrast-disparity-decoding model. The results suggested that the contrast and disparity in 3D images were mainly represented in the early and dorsal visual regions separately. The two visual systems may interact with each other to decode 3D-contrast images.

Alterations in resting-state network dynamics along the Alzheimer\u2019s disease continuum

Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD. We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with subjective cognitive decline (SCD), 10 patients with amnestic mild cognitive impairment (aMCI) and 10 patients with typical Alzheimer’s disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. Correlations were sought between temporal properties of HMM states and participants' cognitive test scores, whole hippocampal grey matter volume and regional brain glucose metabolism. The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores, nor with the whole hippocampal volume. However, it correlated positively with the regional glucose consumption in the right dorsolateral prefrontal cortex (DLPFC). AD patients present alterations of posterior DMN power activation dynamics at rest that identify an additional electrophysiological correlate of AD-related synaptic and neural dysfunction. The right DLPFC may play a causal role in the activation of the posterior DMN, possibly linked to the occurrence of mind wandering episodes. As such, these data might suggest a neural correlate of the decrease in mind wandering episodes reported in pathological aging.

Rapid acquisition of novel written word-forms: ERP evidence

BackgroundNovel word acquisition is generally believed to be a rapid process, essential for ensuring a flexible and efficient communication system; at least in spoken language, learners are able to construct memory traces for new linguistic stimuli after just a few exposures. However, such rapid word learning has not been systematically found in visual domain, with different confounding factors obscuring the orthographic learning of novel words. This study explored the changes in human brain activity occurring online, during a brief training with novel written word-forms using a silent reading taskResultsSingle-trial, cluster-based random permutation analysis revealed that training caused an extremely fast (after just one repetition) and stable facilitation in novel word processing, reflected in the modulation of P200 and N400 components, possibly indicating rapid dynamics at early and late stages of the lexical processing. Furthermore, neural source estimation of these effects revealed the recruitment of brain areas involved in orthographic and lexico-semantic processing, respectively.ConclusionsThese results suggest the formation of neural memory traces for novel written word-forms after a minimal exposure to them even in the absence of a semantic reference, resembling the rapid learning processes known to occur in spoken language.

Deep Natural Image Reconstruction from Human Brain Activity Based on Conditional Progressively Growing Generative Adversarial Networks.

Brain decoding based on functional magnetic resonance imaging has recently enabled the identification of visual perception and mental states. However, due to the limitations of sample size and the lack of an effective reconstruction model, accurate reconstruction of natural images is still a major challenge. The current, rapid development of deep learning models provides the possibility of overcoming these obstacles. Here, we propose a deep learning-based framework that includes a latent feature extractor, a latent feature decoder, and a natural image generator, to achieve the accurate reconstruction of natural images from brain activity. The latent feature extractor is used to extract the latent features of natural images. The latent feature decoder predicts the latent features of natural images based on the response signals from the higher visual cortex. The natural image generator is applied to generate reconstructed images from the predicted latent features of natural images and the response signals from the visual cortex. Quantitative and qualitative evaluations were conducted with test images. The results showed that the reconstructed image achieved comparable, accurate reproduction of the presented image in both high-level semantic category information and low-level pixel information. The framework we propose shows promise for decoding the brain activity.

P.009 MDK gene expression is correlated with human brain activity during acceptance of monetary rewards

Computationally efficient methods for bridging length scales, from highly resolved micro/meso-scale models that can explicitly model crack growth, to macro-scale continuum models that are more suitable for modeling large parts, have been of interest to researchers for decades. In this work, an improved brittle damage model is presented for the simulation of dynamic fracture in continuum scale quasi-brittle metal components. Crack evolution statistics, including the number, length, and orientation of individual cracks, are extracted from high-fidelity, finite discrete element method (FDEM) simulations and used to generate effective material moduli that reflect the material’s damaged state over time. This strategy allows for the retention of small-scale physical behaviors such as crack growth and coalescence in continuum scale hydrodynamic simulations. However, the high-fidelity simulations required to generate the crack statistics are computationally expensive. Thus, steps were taken to produce a flexible constitutive model to reduce the number of costly high-fidelity simulations needed to produce accurate results. A new stress based degradation criterion is introduced for the degradation of individual material zones. This allows for the development of a heterogeneous damage distribution within the bulk material. Then a flow stress model is added to the hydrodynamic simulation to account for plasticity in quasi-brittle materials. As a result, the effective moduli model can be applied to a larger range of materials. The effective moduli constitutive model is used to simulate beryllium flyer plate experiments. The results from the continuum scale simulations using statistics from a single high-fidelity simulation are found to be in excellent agreement with numerical and experimental velocity interferometer data. The same set of crack statistics are used to extrapolate the results of a higher rate flyer plate case using the effective moduli model. The extension of this model to higher rate cases shows promise for further reducing the number of costly high-fidelity simulations needed to generate crack statistics.

ERP CORE: An open resource for human event-related potential research

Event-related potentials (ERPs) are noninvasive measures of human brain activity that index a range of sensory, cognitive, affective, and motor processes. Despite their broad application across basic and clinical research, there is little standardization of ERP paradigms and analysis protocols across studies. To address this, we created ERP CORE (Compendium of Open Resources and Experiments), a set of optimized paradigms, experiment control scripts, data processing pipelines, and sample data (N = 40 neurotypical young adults) for seven widely used ERP components: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). This resource makes it possible for researchers to 1) employ standardized ERP paradigms in their research, 2) apply carefully designed analysis pipelines and use a priori selected parameters for data processing, 3) rigorously assess the quality of their data, and 4) test new analytic techniques with standardized data from a wide range of paradigms.

Tracking spatial and non-spatial working memory representations coded in human oscillatory brain activity

Tracking spatial and non-spatial working memory representations coded in human oscillatory brain activity

Structured Neural Decoding With Multitask Transfer Learning of Deep Neural Network Representations.

The reconstruction of visual information from human brain activity is a very important research topic in brain decoding. Existing methods ignore the structural information underlying the brain activities and the visual features, which severely limits their performance and interpretability. Here, we propose a hierarchically structured neural decoding framework by using multitask transfer learning of deep neural network (DNN) representations and a matrix-variate Gaussian prior. Our framework consists of two stages, Voxel2Unit and Unit2Pixel. In Voxel2Unit, we decode the functional magnetic resonance imaging (fMRI) data to the intermediate features of a pretrained convolutional neural network (CNN). In Unit2Pixel, we further invert the predicted CNN features back to the visual images. Matrix-variate Gaussian prior allows us to take into account the structures between feature dimensions and between regression tasks, which are useful for improving decoding effectiveness and interpretability. This is in contrast with the existing single-output regression models that usually ignore these structures. We conduct extensive experiments on two real-world fMRI data sets, and the results show that our method can predict CNN features more accurately and reconstruct the perceived natural images and faces with higher quality.

Building an Ear-EEG System by Hacking a Commercial Neck Speaker and a Commercial EEG Amplifier to Record Brain Activity Beyond the Lab

Electroencephalography (EEG) allows measuring human brain activity for scientific, diagnostic and therapeutic purposes. For scientific purposes, EEG is traditionally used to study the brain-behaviour relationship under well controlled laboratory conditions while the participant sits quietly in a room. Consequently, our knowledge regarding the brain-behaviour relationship was limited to human behaviour under constrained conditions. The development of mobile EEG hardware in the last years allows moving beyond the lab and studying brain activity in everyday life. Most EEG systems record from electrodes that are distributed over the head, kept in place with an elastic cap or net. For long term EEG recordings, to study natural behaviour in public, these systems are not optimal, as they are rather bulky and unpleasant to wear for longer periods. We argue for “transparent”, ear-centered EEG systems instead. Transparent in the sense that both the person wearing the EEG system as well as the people interacting with that person are not disturbed by it. We describe here the integration of a commercially available research-grade mobile EEG amplifier and a commercially available neck speaker as a mounting system to be used in combination with ear electrodes (cEEGrid). This nEEGlace can be used for ear-EEG recordings in public over extended periods of time. The integrated microphone and the integrated speakers provide the possibility to play and record sound. This allows conducting experiments on auditory perception beyond the lab by both studying the brain responses to naturally occurring sounds as well as to experimental auditory stimuli. We describe the general design, present a working prototype, outline a number of use cases and discuss future improvements of the proposed design.

Human Chemosignals and Brain Activity: A Preliminary Meta-analysis of the Processing of Human Body Odors.

Across phyla, chemosignals are a widely used form of social communication and increasing evidence suggests that chemosensory communication is present also in humans. Chemosignals can transfer, via body odors, socially relevant information, such as specific information about identity or emotional states. However, findings on neural correlates of processing of body odors are divergent. The aims of this meta-analysis were to assess the brain areas involved in the perception of body odors (both neutral and emotional) and the specific activation patterns for the perception of neutral body odor (NBO) and emotional body odor (EBO). We conducted an activation likelihood estimation (ALE) meta-analysis on 16 experiments (13 studies) examining brain activity during body odors processing. We found that the contrast EBO versus NBO resulted in significant convergence in the right middle frontal gyrus and the left cerebellum, whereas the pooled meta-analysis combining all the studies of human odors showed significant convergence in the right inferior frontal gyrus. No significant cluster was found for NBOs. However, our findings also highlight methodological heterogeneity across the existing literature. Further neuroimaging studies are needed to clarify and support the existing findings on neural correlates of processing of body odors.

Performance vs. competence in human–machine comparisons

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their underlying capacities, vs. when such failures are only superficial or peripheral? This article draws on a foundational insight from cognitive science—the distinction between performance and competence—to encourage “species-fair” comparisons between humans and machines. The performance/competence distinction urges us to consider whether the failure of a system to behave as ideally hypothesized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of human–machine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring.