AIMC Topic: Electroencephalography

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Progress in EEG-Based Brain Robot Interaction Systems.

Computational intelligence and neuroscience
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is pr...

Correlates of sleep quality in midlife and beyond: a machine learning analysis.

Sleep medicine
OBJECTIVES: In older adults, traditional metrics derived from polysomnography (PSG) are not well correlated with subjective sleep quality. Little is known about whether the association between PSG and subjective sleep quality changes with age, or whe...

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must b...

Quantitative EEG for Predicting Upper Limb Motor Recovery in Chronic Stroke Robot-Assisted Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Stroke is a leading cause for adult disability, which in many cases causes motor deficits. Despite the developments in motor rehabilitation techniques, recovery of upper limb functions after stroke is limited and heterogeneous in terms of outcomes, a...

A convolutional neural network for steady state visual evoked potential classification under ambulatory environment.

PloS one
The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)...

Subject-based discriminative sparse representation model for detection of concealed information.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each s...

Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy.

Journal of neuroscience methods
BACKGROUND: Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized ...

Classifier transfer with data selection strategies for online support vector machine classification with class imbalance.

Journal of neural engineering
OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the sup...

Evolving Network Model That Almost Regenerates Epileptic Data.

Neural computation
In many realistic networks, the edges representing the interactions between nodes are time varying. Evidence is growing that the complex network that models the dynamics of the human brain has time-varying interconnections, that is, the network is ev...

Automated seizure detection using limited-channel EEG and non-linear dimension reduction.

Computers in biology and medicine
Electroencephalography (EEG) is an essential component in evaluation of epilepsy. However, full-channel EEG signals recorded from 18 to 23 electrodes on the scalp is neither wearable nor computationally effective. This paper presents advantages of bo...