AIMC Topic: Evoked Potentials
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Which Visual Modality Is Important When Judging the Naturalness of the Agent (Artificial Versus Human Intelligence) Providing Recommendations in the Symbolic Consumption Context?
Sensors (Basel, Switzerland)
Sep 3, 2020
This study aimed to explore how the type and visual modality of a recommendation agent's identity affect male university students' (1) self-reported responses to agent-recommended symbolic brand in evaluating the naturalness of virtual agents, human,...
The Effect of the Graphic Structures of Humanoid Robot on N200 and P300 Potentials.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 20, 2020
Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, t...
Decoding attention control and selection in visual spatial attention.
Human brain mapping
Jun 16, 2020
Event-related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The univariate ERP approach, however, has left important questions inadequately answered. We addressed two questions by appl...
Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of h...
Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.
Scientific reports
Nov 22, 2019
Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to class...
Humans
Biomedical Engineering
Electrodes, Implanted
Electrophysiological Phenomena
Electroencephalography
Task Performance and Analysis
Sensitivity and Specificity
Epilepsy
Evoked Potentials
Cognition
Verbal Behavior
Memory, Short-Term
Unsupervised Machine Learning
Algorithms
Electrocorticography
Datasets as Topic
Brain Mapping
Brain
Retrospective Studies
Predicting individual decision-making responses based on single-trial EEG.
NeuroImage
Nov 4, 2019
Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual's decision-making response (i.e., acceptance or rejection). W...
Post-hoc modification of linear models: Combining machine learning with domain information to make solid inferences from noisy data.
NeuroImage
Sep 26, 2019
Linear machine learning models "learn" a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the de...
Meaning-driven syntactic predictions in a parallel processing architecture: Theory and algorithmic modeling of ERP effects.
Neuropsychologia
May 14, 2019
Syntactic and semantic information processing can interact selectively during language comprehension. However, the nature and extent of the interactions, in particular of semantic effects on syntax, remain to some extent elusive. We revisit an influe...
Adaptive neural network classifier for decoding MEG signals.
NeuroImage
May 4, 2019
We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowin...
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach.
IEEE transactions on bio-medical engineering
Apr 29, 2019
OBJECTIVE: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, ...
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