AIMC Topic: Electroencephalography

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Designing a brain computer interface for control of an assistive robotic manipulator using steady state visually evoked potentials.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
An assistive robotic manipulator (ARM) can provide independence and improve the quality of life for patients suffering from tetraplegia. However, to properly control such device to a satisfactory level without any motor functions requires a very high...

Movement related activity in the μ band of the human EEG during a robot-based proprioceptive task.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Innovative research in the fields of prosthetic, neurorehabilitation, motor control and human physiology has been focusing on the study of proprioception, the sense through which we perceive the position and movement of our body, and great achievemen...

Investigation of Fatigue Using Different EMG Features.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Rehabilitative exercise for people suffering from upper limb impairments has the potential to improve their neuro-plasticity due to repetitive training. Our study investigates the usefulness of Electroencephalogram and Electromyogram (EMG) signals fo...

Intracerebral EEG Artifact Identification Using Convolutional Neural Networks.

Neuroinformatics
Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifa...

Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG.

Current pharmaceutical biotechnology
BACKGROUND: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble ...

Facial expression recognition based on Electroencephalogram and facial landmark localization.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Facial expression recognition plays an essential role in affective computing, mental illness diagnosis and rehabilitation. Therefore, facial expression recognition has attracted more and more attention over the years.

A Multi-View Deep Learning Framework for EEG Seizure Detection.

IEEE journal of biomedical and health informatics
The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual in...

The Decision Decoding ToolBOX (DDTBOX) - A Multivariate Pattern Analysis Toolbox for Event-Related Potentials.

Neuroinformatics
In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG...

Expert-level sleep scoring with deep neural networks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the com...

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

Neurosurgery
BACKGROUND: Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but i...