AIMC Topic: Electroencephalography
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Robot-assisted vs. manually guided stereoelectroencephalography for refractory epilepsy: a systematic review and meta-analysis.
Neurosurgical review
May 3, 2023
Robotic assistance has improved electrode implantation precision in stereoelectroencephalography (SEEG) for refractory epilepsy patients. We sought to assess the relative safety of the robotic-assisted (RA) procedure compared to the traditional hand-...
Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm.
Artificial intelligence in medicine
May 2, 2023
BACKGROUND: Proper maintenance of hypnosis is crucial for ensuring the safety of patients undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been developed to monitor hypnotic levels. However, the black-box nature o...
A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions.
Sensors (Basel, Switzerland)
Apr 28, 2023
Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion toward...
Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy.
Epilepsy & behavior : E&B
Apr 27, 2023
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence a...
Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.
Medical & biological engineering & computing
Apr 25, 2023
Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the applica...
Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.
Scientific reports
Apr 25, 2023
Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalogr...
Rethinking Saliency Map: A Context-Aware Perturbation Method to Explain EEG-Based Deep Learning Model.
IEEE transactions on bio-medical engineering
Apr 20, 2023
Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically study how to explain EEG-based deep learning models. In this paper, we review the related works that attempt to explain EEG-...
Encoding of speech in convolutional layers and the brain stem based on language experience.
Scientific reports
Apr 20, 2023
Comparing artificial neural networks with outputs of neuroimaging techniques has recently seen substantial advances in (computer) vision and text-based language models. Here, we propose a framework to compare biological and artificial neural computat...
Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 19, 2023
The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial...
Parkinson's disease detection and classification using EEG based on deep CNN-LSTM model.
Biotechnology & genetic engineering reviews
Apr 11, 2023
The progressive loss of motor function in the brain is a hallmark of Parkinson's disease (PD). Electroencephalogram (EEG) signals are commonly used for early diagnosis since they are associated with a brain disorder. This work aims to find a better w...
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