AIMC Topic: Electroencephalography
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A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.
IEEE transactions on cybernetics
Oct 12, 2021
Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue ...
Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network.
Computational and mathematical methods in medicine
Oct 11, 2021
Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely us...
Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.
Sensors (Basel, Switzerland)
Oct 9, 2021
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness a...
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI a...
Predicting Human Intention-Behavior Through EEG Signal Analysis Using Multi-Scale CNN.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional n...
A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN...
Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computin...
Advanced Machine-Learning Methods for Brain-Computer Interfacing.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroenc...
A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EE...
Epilepsy Signal Recognition Using Online Transfer TSK Fuzzy Classifier Underlying Classification Error and Joint Distribution Consensus Regularization.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 7, 2021
In this study, an online transfer TSK fuzzy classifier O-T-TSK-FC is proposed for recognizing epilepsy signals. Compared with most of the existing transfer learning models, O-T-TSK-FC enjoys its merits from the following three aspects: 1) Since diffe...
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