AIMC Topic: Electroencephalography
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EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.
Computational and mathematical methods in medicine
Sep 28, 2015
In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. How...
An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.
International journal of neural systems
Sep 14, 2015
Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recor...
Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 10, 2015
Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain-computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback o...
Learning to decode human emotions with Echo State Networks.
Neural networks : the official journal of the International Neural Network Society
Sep 7, 2015
The aim of this paper is to identify the common neural signatures based on which the positive and negative valence of human emotions across multiple subjects can be reliably discriminated. The brain activity is observed via Event Related Potentials (...
Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification.
Medical & biological engineering & computing
Aug 22, 2015
In this paper, a novel hierarchical multi-class SVM (H-MSVM) with extreme learning machine (ELM) as kernel is proposed to classify electroencephalogram (EEG) signals for epileptic seizure detection. A clinical EEG benchmark dataset having five classe...
A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.
Biomedical engineering online
Aug 20, 2015
BACKGROUND: A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although in...
Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.
PloS one
Aug 4, 2015
Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identificatio...
Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.
Journal of neuroscience methods
Jul 17, 2015
BACKGROUND: Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state...
Leveraging anatomical information to improve transfer learning in brain-computer interfaces.
Journal of neural engineering
Jul 14, 2015
OBJECTIVE: Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research ...
Assessment of brain-machine interfaces from the perspective of people with paralysis.
Journal of neural engineering
Jul 14, 2015
OBJECTIVE: One of the main goals of brain-machine interface (BMI) research is to restore function to people with paralysis. Currently, multiple BMI design features are being investigated, based on various input modalities (externally applied and surg...
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