AIMC Topic: Electroencephalography

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Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale ...

Relative wave energy-based adaptive neuro-fuzzy inference system for estimation of the depth of anaesthesia.

Journal of integrative neuroscience
The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent model...

Robotic-Assisted and Image-Guided MRI-Compatible Stereoelectroencephalography.

The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques
BACKGROUND: Stereoelectroencephalography has been in regular use at the Montreal Neurological Institute since 1972. The technique has been in constant evolution to incorporate advances in materials, imaging, and robotics technology. MRI-compatible el...

A brain-controlled lower-limb exoskeleton for human gait training.

The Review of scientific instruments
Brain-computer interfaces have been a novel approach to translate human intentions into movement commands in robotic systems. This paper describes an electroencephalogram-based brain-controlled lower-limb exoskeleton for gait training, as a proof of ...

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features.

Journal of neural engineering
OBJECTIVE: Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However,...

Epileptic seizure detection based on the kernel extreme learning machine.

Technology and health care : official journal of the European Society for Engineering and Medicine
This paper presents a pattern recognition model using multiple features and the kernel extreme learning machine (ELM), improving the accuracy of automatic epilepsy diagnosis. After simple preprocessing, temporal- and wavelet-based features are extrac...

Real-time analysis on ensemble SVM scores to reduce P300-Speller intensification time.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In most Brain-Computer Interface systems, especially the P300-Speller, there must be a harmonized balance between the accuracy and the spelling time. One major drawback of the classical 36-choice P300-Speller is the slow rate of character elicitation...

A separated feature learning based DBN structure for classification of SSMVEP signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Signal processing is one of the key points in brain computer interface (BCI) application. The common methods in BCI signal classification include canonical correlation analysis (CCA), support vector machine (SVM) and so on. However, because BCI signa...