Academic Articles & Research Papers

Maternal cardiovascular adaptation to twin pregnancy: a population-based prospective cohort study

Abstract Background In women with singleton pregnancies, maternal adaptation is considered a stress test for later life cardiovascular disease. The aim of this study was to assess maternal adaptation in women with twin pregnancies compared ...

A Novel Depression Rehabilitation Game Framework Using VR Based on Neurofeedback

At present,depression is treated with antidepressant drugs and assisted with psychotherapy and physical therapy,and these expensive and time-consuming treatment methods often end prematurely and result in a prolonged course without effectiv...

EEG-based deep learning estimates preterm infants’ cerebral age: Contribution of slow-wave and cross-frequency coupling.

Mapping the monthly habitat dynamics of greater amberjack using MaxEnt model in the Taiwan Strait: Implications for sustainable blue transformations.

Ten rules for robust taxonomy in ecological datasets.

Diminished Auditory Responses during NREM Sleep Correlate with the Hierarchy of Language Processing.

Natural sleep provides a powerful model system for studying the neuronal correlates of awareness and state changes in the human brain. To quantitatively map the nature of sleep-induced modulations in sensory responses we presented participa...

Repeated prefrontal transcranial direct current stimulation improves behavioral regulation and modulates resting-state EEG microstate dynamics in young adults with problematic smartphone use: a randomized sham-controlled longitudinal study.

GROND: automated characterization of periodic and rhythmic EEG patterns.

Different modes of stimulation at the Neiguan acupoint (PC6) regulate different EEG microstates under VR immersion conditions.

The Dynamic Properties of a Brain Network During Spatial Working Memory Tasks in College Students With ADHD Traits

This study investigated deficits of spatial working memory in college students with attention-deficit/hyperactivity disorder (ADHD) traits using event-related potentials (ERPs) and the spatial 2-back task. We also computed sensory-level act...

A role for neural modeling in the study of brain disorders: Comment on From intracerebral EEG signals to brain connectivity: identification of epileptogenic networks in partial epilepsy by F. Wendling et al.

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across...

Hierarchical Multimodal Sleep Staging with Optimized EEG, EOG, and PPG Features for Wearable Applications

Automatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with compu...

Deep learning for electroencephalogram (EEG) classification tasks: a review

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses signi...

GAUSSIAN PROCESSES FOR MACHINE LEARNING

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many dee...

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images i...

Proceedings of the 25th international conference on Machine learning

This volume contains the papers accepted to the 25th International Conference on Machine Learning (ICML 2008). ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and...

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems...

Machine learning applications in epilepsy

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...