AIMC Topic: Evoked Potentials
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Auditory prediction errors as individual biomarkers of schizophrenia.
NeuroImage. Clinical
May 3, 2017
Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible ...
Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees.
PloS one
Apr 13, 2017
OBJECTIVE: Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-tr...
Improving zero-training brain-computer interfaces by mixing model estimators.
Journal of neural engineering
Mar 13, 2017
OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders ...
Female
Reproducibility of Results
Adult
Sensitivity and Specificity
Machine Learning
Algorithms
Brain
Computer Simulation
Brain-Computer Interfaces
Task Performance and Analysis
Evoked Potentials
Communication Devices for People with Disabilities
Male
Models, Statistical
Data Interpretation, Statistical
Pattern Recognition, Automated
Humans
Sparse and Specific Coding during Information Transmission between Co-cultured Dentate Gyrus and CA3 Hippocampal Networks.
Frontiers in neural circuits
Mar 6, 2017
To better understand encoding and decoding of stimulus information in two specific hippocampal sub-regions, we isolated and co-cultured rat primary dentate gyrus (DG) and CA3 neurons within a two-chamber device with axonal connectivity via micro-tunn...
Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review.
Journal of neural engineering
Jan 9, 2017
Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently ...
Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity.
Proceedings of the National Academy of Sciences of the United States of America
Dec 12, 2016
The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-o...
Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension.
Cognition
Nov 18, 2016
The study of the N400 event-related brain potential has provided fundamental insights into the nature of real-time comprehension processes, and its amplitude is modulated by a wide variety of stimulus and context factors. It is generally thought to r...
The Sense of Agency as Tracking Control.
PloS one
Oct 14, 2016
Does sense of agency (SoA) arise merely from action-outcome associations, or does an additional real-time process track each step along the chain? Tracking control predicts that deviant intermediate steps between action and outcome should reduce SoA....
A neuromorphic model of motor overflow in focal hand dystonia due to correlated sensory input.
Journal of neural engineering
Aug 31, 2016
OBJECTIVE: Motor overflow is a common and frustrating symptom of dystonia, manifested as unintentional muscle contraction that occurs during an intended voluntary movement. Although it is suspected that motor overflow is due to cortical disorganizati...
Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.
Schizophrenia research
Jul 15, 2016
Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG...
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