AIMC Topic: Electroencephalography

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Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data.

EBioMedicine Mar 18, 2021
BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable t...
Seizures Neurologists Brain Reproducibility of Results Electroencephalography Epilepsy Deep Learning Artificial Intelligence Humans Data Analysis Algorithms
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Semi-dilated convolutional neural networks for epileptic seizure prediction.

Neural networks : the official journal of the International Neural Network Society Mar 17, 2021
Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epileptic seizures holds great potential for improving the quality of life of people with epilepsy, but seizure prediction solely from the Electroencephal...
Humans Neural Networks, Computer Electroencephalography Seizures Brain Wavelet Analysis
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Multitask Feature Learning Meets Robust Tensor Decomposition for EEG Classification.

IEEE transactions on cybernetics Mar 17, 2021
In this article, we study a tensor-based multitask learning (MTL) method for classification. Taking into account the fact that in many real-world applications, the given training samples are limited and can be inherently arranged into multidimensiona...
Algorithms Electroencephalography Signal Processing, Computer-Assisted Humans Machine Learning
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How can the accuracy of SEEG be increased?-an analysis of the accuracy of multilobe-spanning SEEG electrodes based on a frameless stereotactic robot-assisted system.

Annals of palliative medicine Mar 10, 2021
BACKGROUND: A frameless stereotactic robot-assisted system allows stereoelectroencephalography (SEEG) electrodes to span multiple lobes. As the angularity and length are increased, maintaining accuracy of the electrodes becomes more challenging. The ...
Adult Adolescent Child, Preschool Female Young Adult Electroencephalography Stereotaxic Techniques Drug Resistant Epilepsy Child Robotics Humans Electrodes, Implanted
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Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels.

Journal of neuroscience methods Mar 9, 2021
BACKGROUND: In clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpola...
Databases, Factual Electroencephalography Artifacts Electrodes Humans Neural Networks, Computer
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A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Journal of neural engineering Mar 5, 2021
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning technique...
Deep Learning Humans Electroencephalography Brain-Computer Interfaces Algorithms Brain
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Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach.

Brain topography Mar 4, 2021
Patients with stroke can experience a drastic change in their body representation (BR), beyond the physical and psychological consequences of stroke itself. Noteworthy, the misperception of BR could affect patients' motor performance even more. Our s...
Robotic Surgical Procedures Robotics Gait Body Image Stroke Electroencephalography Humans
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A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.

Sensors (Basel, Switzerland) Mar 1, 2021
Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods ...
Electroencephalography Arousal Machine Learning Emotions Humans Neural Networks, Computer
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Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.

The American journal of emergency medicine Feb 25, 2021
BACKGROUND: Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving outcomes in cardiac arrest. Cerebral perfusion pressure (CePP) could represent cerebral circulation during CPR, but it is difficult to measure non-inv...
Ventricular Fibrillation Disease Models, Animal Electroencephalography Animals Cerebrovascular Circulation Machine Learning Cross-Over Studies Swine Cardiopulmonary Resuscitation Female
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The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

Sensors (Basel, Switzerland) Feb 25, 2021
In addition to helping develop products that aid the disabled, brain-computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance...
Reproducibility of Results Deep Learning Bayes Theorem Humans Brain-Computer Interfaces Electroencephalography
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