AIMC Topic: Electroencephalography

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Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability.

Sensors (Basel, Switzerland) Nov 8, 2023
Predicting pilots' mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which a...
Electroencephalography Attention Neural Networks, Computer Machine Learning
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IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.

Computer methods in biomechanics and biomedical engineering Nov 7, 2023
As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for...
Imagination Algorithms Electroencephalography Signal Processing, Computer-Assisted Humans Brain-Computer Interfaces Neural Networks, Computer
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Aided diagnosis of cervical spondylotic myelopathy using deep learning methods based on electroencephalography.

Medical engineering & physics Nov 7, 2023
Cervical spondylotic myelopathy (CSM) is the most severe type of cervical spondylosis. It is challenging to achieve early diagnosis with current clinical diagnostic tools. In this paper, we propose an end-to-end deep learning approach for early diagn...
Spondylosis Humans Cervical Vertebrae Neural Networks, Computer Spinal Cord Diseases Deep Learning Electroencephalography
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A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate.

Computer methods in biomechanics and biomedical engineering Nov 6, 2023
In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased huma...
Neural Networks, Computer Brain Humans Signal Processing, Computer-Assisted Deep Learning Heart Rate Stress, Physiological Electroencephalography
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A Deep Learning Framework for Anesthesia Depth Prediction from Drug Infusion History.

Sensors (Basel, Switzerland) Nov 6, 2023
In the target-controlled infusion (TCI) of propofol and remifentanil intravenous anesthesia, accurate prediction of the depth of anesthesia (DOA) is very challenging. Patients with different physiological characteristics have inconsistent pharmacodyn...
Anesthetics, Intravenous Piperidines Remifentanil Electroencephalography Anesthesia Humans Aged Deep Learning Propofol
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ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.

Computers in biology and medicine Nov 2, 2023
OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signal...
Movement Imagination Electroencephalography Brain-Computer Interfaces Humans Machine Learning Neural Networks, Computer Algorithms
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Bridging Neuroscience and Robotics: Spiking Neural Networks in Action.

Sensors (Basel, Switzerland) Nov 1, 2023
Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area that requires development is the ability to act in dynamically changing environments. To advance this, developments have turned towards understanding t...
Neural Networks, Computer Brain Computer Simulation Humans Robotics Electroencephalography
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Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Sensors (Basel, Switzerland) Oct 23, 2023
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and sub...
Depressive Disorder Deep Learning Humans Electroencephalography Neural Networks, Computer
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Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

Journal of robotic surgery Oct 21, 2023
The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11...
Robotics Humans Electroencephalography Machine Learning Surgeons Robotic Surgical Procedures Clinical Competence Female
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An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.

Scientific reports Oct 18, 2023
Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area...
Machine Learning Deep Learning Neural Networks, Computer Electroencephalography Algorithms Reproducibility of Results
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