AIMC Topic: Electroencephalography
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Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Nov 20, 2015
The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibra...
Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.
IEEE transactions on neural networks and learning systems
Nov 18, 2015
This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics...
Towards a symbiotic brain-computer interface: exploring the application-decoder interaction.
Journal of neural engineering
Nov 18, 2015
OBJECTIVE: State of the art brain-computer interface (BCI) research focuses on improving individual components such as the application or the decoder that converts the user's brain activity to control signals. In this study, we investigate the intera...
Comparative Study of SSVEP- and P300-Based Models for the Telepresence Control of Humanoid Robots.
PloS one
Nov 12, 2015
In this paper, we evaluate the control performance of SSVEP (steady-state visual evoked potential)- and P300-based models using Cerebot-a mind-controlled humanoid robot platform. Seven subjects with diverse experience participated in experiments conc...
Learning Recurrent Waveforms Within EEGs.
IEEE transactions on bio-medical engineering
Nov 10, 2015
GOAL: We demonstrate an algorithm to automatically learn the time-limited waveforms associated with phasic events that repeatedly appear throughout an electroencephalogram.
Measuring empathy for human and robot hand pain using electroencephalography.
Scientific reports
Nov 3, 2015
This study provides the first physiological evidence of humans' ability to empathize with robot pain and highlights the difference in empathy for humans and robots. We performed electroencephalography in 15 healthy adults who observed either human- o...
Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines.
NeuroImage
Oct 24, 2015
A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decodin...
Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications.
Neural networks : the official journal of the International Neural Network Society
Oct 17, 2015
The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to de...
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders.
Annals of biomedical engineering
Oct 13, 2015
We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine m...
Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.
The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
Oct 9, 2015
OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.
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