AIMC Topic: Mental Processes
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Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.
Computers in biology and medicine
Aug 4, 2019
BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from health...
Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 26, 2019
People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its...
Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 25, 2019
Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficie...
A scoping review of ontologies related to human behaviour change.
Nature human behaviour
Jan 14, 2019
Ontologies are classification systems specifying entities, definitions and inter-relationships for a given domain, with the potential to advance knowledge about human behaviour change. A scoping review was conducted to: (1) identify what ontologies e...
Task activations produce spurious but systematic inflation of task functional connectivity estimates.
NeuroImage
Dec 28, 2018
Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (...
Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 3, 2018
Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload ...
Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications.
Computational intelligence and neuroscience
Apr 19, 2018
Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Due to variabilities in fMRI data and the limitation of the collection of human fMRI data, it is not easy to tr...
Mind Games: Game Engines as an Architecture for Intuitive Physics.
Trends in cognitive sciences
Jun 24, 2017
We explore the hypothesis that many intuitive physical inferences are based on a mental physics engine that is analogous in many ways to the machine physics engines used in building interactive video games. We describe the key features of game physic...
Multistability of the Brain Network for Self-other Processing.
Scientific reports
Mar 3, 2017
Early fMRI studies suggested that brain areas processing self-related and other-related information were highly overlapping. Hypothesising functional localisation of the cortex, researchers have tried to locate "self-specific" and "other-specific" re...
Spatially regularized machine learning for task and resting-state fMRI.
Journal of neuroscience methods
Oct 16, 2015
BACKGROUND: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades.
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