AIMC Topic: Electroencephalography

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Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.

PloS one Aug 26, 2020
Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signal...
Support Vector Machine Monitoring, Physiologic Anesthesia, General Algorithms Monitoring, Intraoperative Neural Networks, Computer Machine Learning Humans Electroencephalography Evoked Potentials, Auditory Anesthetics, Intravenous Consciousness Consciousness Monitors
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The EEG Signal Analysis for Spatial Cognitive Ability Evaluation Based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 24, 2020
This study aims to find an effective method to evaluate the efficacy of cognitive training of spatial memory under a virtual reality environment, by classifying the EEG signals of subjects in the early and late stages of spatial cognitive training. T...
Electroencephalography Neural Networks, Computer Cognition
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Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.

Parkinsonism & related disorders Aug 23, 2020
OBJECTIVE: We have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-coding EEG Algorithm for PD (LEAPD). This method efficiently encodes EEG time series into features that ...
Sensitivity and Specificity Male Signal Processing, Computer-Assisted Parkinson Disease Middle Aged Humans Reproducibility of Results Electroencephalography Aged Female Machine Learning
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The Bayesian brain in imbalance: Medial, lateral and descending pathways in tinnitus and pain: A perspective.

Progress in brain research Aug 21, 2020
Tinnitus and pain share similarities in their anatomy, pathophysiology, clinical picture and treatments. Based on what is known in the pain field, a heuristic model can be proposed for the pathophysiolgy of tinnitus. This heuristic pathophysiological...
Pain Tinnitus Humans Bayes Theorem Brain Electroencephalography
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Emotional EEG classification using connectivity features and convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society Aug 19, 2020
Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-dimensional raw data. However,...
Emotions Neural Networks, Computer Electroencephalography Algorithms Brain Brain-Computer Interfaces Humans
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Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Journal of neural engineering Aug 19, 2020
OBJECTIVE: A hearing aid's noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-st...
Deep Learning Attention Acoustic Stimulation Speech Perception Speech Electroencephalography
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Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke.

Topics in stroke rehabilitation Aug 17, 2020
In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, electrical patterns in the brain have emerged as a marker for studying changes in the brain associated...
Stroke Stroke Rehabilitation Electroencephalography Robotic Surgical Procedures Magnetic Resonance Imaging Humans Robotics
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Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.

Sensors (Basel, Switzerland) Aug 17, 2020
Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain-computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across s...
Speech Electroencephalography Brain-Computer Interfaces Machine Learning Deep Learning Neural Networks, Computer
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Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction.

Journal of neuroscience methods Aug 15, 2020
BACKGROUND: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intentio...
Electroencephalography Stroke Intention Brain Robotics Brain-Computer Interfaces Stroke Rehabilitation Humans Algorithms
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Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders.

Progress in neuro-psychopharmacology & biological psychiatry Aug 14, 2020
Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alpha power in psychosis indicate an abnormal arousal state. We investigated brain activity across the ...
Humans Machine Learning Databases, Factual Schizophrenia Adolescent Child Brain Alpha Rhythm Male Electroencephalography
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