AIMC Topic: Electroencephalography
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Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 14, 2023
In electroencephalography (EEG) classification paradigms, data from a target subject is often difficult to obtain, leading to difficulties in training a robust deep learning network. Transfer learning and their variations are effective tools in impro...
Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model.
NeuroImage
Dec 12, 2023
This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decad...
Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 7, 2023
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization...
A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.
Neural networks : the official journal of the International Neural Network Society
Dec 6, 2023
Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined wit...
Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.
Sensors (Basel, Switzerland)
Dec 3, 2023
Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an...
An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification.
Artificial intelligence in medicine
Dec 2, 2023
Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-ba...
Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.
IEEE transactions on neural networks and learning systems
Nov 30, 2023
Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes...
MuLHiTA: A Novel Multiclass Classification Framework With Multibranch LSTM and Hierarchical Temporal Attention for Early Detection of Mental Stress.
IEEE transactions on neural networks and learning systems
Nov 30, 2023
Mental stress is an increasingly common psychological issue leading to diseases such as depression, addiction, and heart attack. In this study, an early detection framework based on electroencephalogram (EEG) data is developed for reducing the risk o...
A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management.
Sensors (Basel, Switzerland)
Nov 30, 2023
Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and h...
Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty.
Sensors (Basel, Switzerland)
Nov 29, 2023
In this study, we aim to develop a machine learning model to predict the level of coordination between two players in tacit coordination games by analyzing the similarity of their spatial EEG features. We present an analysis, demonstrating the model'...
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