AIMC Topic: Electroencephalography

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ResNet-50 based technique for EEG image characterization due to varying environmental stimuli.

Computer methods and programs in biomedicine Aug 28, 2022
BACKGROUND AND OBJECTIVE: Emotion is an important factor affecting a person's physical and mental health, but there are few ways to detect a patient's emotions in daily life. Negative emotions not only affect recovery after treatment, but also cause ...
Neural Networks, Computer Algorithms Humans Electroencephalography Support Vector Machine Emotions
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Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study.

NeuroImage. Clinical Aug 27, 2022
Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. How...
Adult Prognosis Electroencephalography Deep Learning Humans Retrospective Studies Neural Networks, Computer
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The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation.

PloS one Aug 22, 2022
Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain...
Deep Brain Stimulation Memory Deep Learning Electroencephalography Electric Stimulation Brain
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CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis.

Scientific reports Aug 19, 2022
Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, na...
Electroencephalography Arousal Humans Neural Networks, Computer Support Vector Machine Emotions
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On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 19, 2022
Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and ...
Algorithms Imagination Deep Learning Electroencephalography Humans Brain-Computer Interfaces
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Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces.

IEEE transactions on cybernetics Aug 18, 2022
Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This...
Neural Networks, Computer Algorithms Electroencephalography Brain Brain-Computer Interfaces Deep Learning
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Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition.

Computers in biology and medicine Aug 17, 2022
In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activi...
Neural Networks, Computer Electroencephalography Arousal Algorithms Emotions
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Effective Evaluation of Medical Images Using Artificial Intelligence Techniques.

Computational intelligence and neuroscience Aug 10, 2022
This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and pre...
Seizures Neural Networks, Computer Parkinson Disease Artificial Intelligence Humans Electroencephalography
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Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure, electroencephalogram, and electrocardiogram: Retrospective study.

PloS one Aug 9, 2022
To develop deep learning models for predicting Interoperative hypotension (IOH) using waveforms from arterial blood pressure (ABP), electrocardiogram (ECG), and electroencephalogram (EEG), and to determine whether combination ABP with EEG or CG impro...
Hypotension Arterial Pressure Deep Learning Electrocardiography Blood Pressure Retrospective Studies Adult Humans Electroencephalography
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A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 4, 2022
The attention mechanism of the Transformer has the advantage of extracting feature correlation in the long-sequence data and visualizing the model. As time-series data, the spatial and temporal dependencies of the EEG signals between the time points ...
Imagination Deep Learning Electroencephalography Brain-Computer Interfaces Movement Humans Algorithms
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