Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms.

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human-computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest.

Privacy-Preserving Domain Adaptation for Motor Imagery-Based Brain-Computer Interfaces.

Objective: Electroencephalogram (EEG) is one of the most widely used signals in motor imagery (MI) based brain-computer interfaces (BCIs). Domain adaptation has been frequently used to improve the accuracy of EEG-based BCIs for a new user (target domain), by making use of labeled data from a previous user (source domain). However, this raises privacy concerns, as EEG contains sensitive health and mental information.

Improving the generalizability of infant psychological research: The ManyBabies model.

Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.

Memetics and neural models of conspiracy theories.

Memetics has so far been developing in social sciences, but to fully understand memetic processes it should be linked to neuroscience models of learning, encoding, and retrieval of memories in the brain. Attractor neural networks show how incoming information is encoded in memory patterns, how it may become distorted, and how chunks of information may form patterns that are activated by many cues, forming the foundation of conspiracy theories. The rapid freezing of high neuroplasticity (RFHN) model is offered as one plausible mechanism of such processes.

Novel Methodological Tools for Behavioral Interventions: The Case of HRV-Biofeedback. Sham Control and Quantitative Physiology-Based Assessment of Training Quality and Fidelity.

Scientific research on heart rate variability (HRV) biofeedback is burdened by certain methodological issues, such as lack of consistent training quality and fidelity assessment or control conditions that would mimic the intervention. In the present study, a novel sham HRV-biofeedback training was proposed as a credible control condition, indistinguishable from the real training. The Yield Efficiency of Training Index (YETI), a quantitative measure based on the spectral distribution of heart rate during training, was suggested for training quality assessment.

Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters.

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting-state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6-min rsEEG blocks of eyes open were analyzed.

Author Correction: Dynamic reconfiguration of functional brain networks during working memory training.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Early deafness leads to re-shaping of functional connectivity beyond the auditory cortex.

Early sensory deprivation, such as deafness, shapes brain development in multiple ways. Deprived auditory areas become engaged in the processing of stimuli from the remaining modalities and in high-level cognitive tasks. Yet, structural and functional changes were also observed in non-deprived brain areas, which may suggest the whole-brain network changes in deaf individuals.

SUPFUNSIM: Spatial Filtering Toolbox for EEG.

Brain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. The SUPFUNSIM library is a new MATLAB toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) measure.

Dynamic reconfiguration of functional brain networks during working memory training.

The functional network of the brain continually adapts to changing environmental demands. The consequence of behavioral automation for task-related functional network architecture remains far from understood. We investigated the neural reflections of behavioral automation as participants mastered a dual n-back task.

Transition of the functional brain network related to increasing cognitive demands.

Network neuroscience provides tools that can easily be used to verify main assumptions of the global workspace theory (GWT), such as the existence of highly segregated information processing during effortless tasks performance, engagement of multiple distributed networks during effortful tasks and the critical role of long-range connections in workspace formation. A number of studies support the assumptions of GWT by showing the reorganization of the whole-brain functional network during cognitive task performance; however, the involvement of specific large scale networks in the formation of workspace is still not well-understood. The aims of our study were: (1) to examine changes in the whole-brain functional network under increased cognitive demands of working memory during an n-back task, and their relationship with behavioral outcomes; and (2) to provide a comprehensive description of local changes that may be involved in the formation of the global workspace, using hub detection and network-based statistic.

Neural network modelling of the influence of channelopathies on reflex visual attention.

This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions.

Visualization for understanding of neurodynamical systems.

Complex neurodynamical systems are quite difficult to analyze and understand. New type of plots are introduced to help in visualization of high-dimensional trajectories and show global picture of the phase space, including relations between basins of attractors. Color recurrence plots (RPs) display distances from each point on the trajectory to all other points in a two-dimensional matrix.

LVQ algorithm with instance weighting for generation of prototype-based rules.

Crisp and fuzzy-logic rules are used for comprehensible representation of data, but rules based on similarity to prototypes are equally useful and much less known. Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with the Learning Vector Quantization (LVQ) algorithm being a prominent example.

Understanding neurodynamical systems via Fuzzy Symbolic Dynamics.

Neurodynamical systems are characterized by a large number of signal streams, measuring activity of individual neurons, local field potentials, aggregated electrical (EEG) or magnetic potentials (MEG), oxygen use (fMRI) or activity of simulated neurons. Various basis set decomposition techniques are used to analyze such signals, trying to discover components that carry meaningful information, but these techniques tell us little about the global activity of the whole system. A novel technique called Fuzzy Symbolic Dynamics (FSD) is introduced to help in understanding of the multidimensional dynamical system's behavior.

Some computational aspects of the brain computer interfaces based on inner music.

We discuss the BCI based on inner tones and inner music. We had some success in the detection of inner tones, the imagined tones which are not sung aloud. Rather easily imagined and controlled, they offer a set of states usable for BCI, with high information capacity and high transfer rates.

Neurolinguistic approach to natural language processing with applications to medical text analysis.

Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. The approximation of this process is of great practical and theoretical interest.

Computational models of dementia and neurological problems.

A critical goal of neuroscience is to fully understand neural processes and their relations to mental processes, and cognitive, affective, and behavioral disorders. Computational modeling, although still in its infancy, continues to play a central role in this endeavor. Presented here is a review of different aspects of computational modeling that help to explain many features of neuropsychological syndromes and psychiatric disease.

Artificial intelligence approaches for rational drug design and discovery.

Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand.