AIMC Topic: Electroencephalography

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Towards a symbiotic brain-computer interface: exploring the application-decoder interaction.

Journal of neural engineering
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
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
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
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
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
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
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
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.

EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.

Computational and mathematical methods in medicine
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
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...