AIMC Topic: Electroencephalography

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Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenar...
Brain-Computer Interfaces Imagination Algorithms Electroencephalography Signal Processing, Computer-Assisted Humans Machine Learning
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Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently d...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Signal-To-Noise Ratio Deep Learning Electroencephalography Machine Learning Algorithms Humans
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Interictal Epileptiform Discharge Detection Using Time-Frequency Analysis and Transfer Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automated detection of IEDs is crucial for assisting clinicians in epilepsy diagnosis as they can help ident...
Signal Processing, Computer-Assisted Epilepsy Seizures Algorithms Time Factors Electroencephalography Humans Machine Learning
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Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are developed to supplement the often inaccurate interview-based risk assessments. Our research goal is to ...
Algorithms Female Male Middle Aged Adult Humans Machine Learning Rest Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted Depressive Disorder, Major Suicidal Ideation
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EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. ...
Electrodes Brain-Computer Interfaces Electroencephalography Signal Processing, Computer-Assisted Humans Algorithms Brain Neural Networks, Computer
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Deep Residual Neural Networks for Spatial EEG Source Imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
EEG source imaging is an indispensable tool for non-invasive study of brain function. Existing methods mainly directly deal with the EEG inverse problem by imposing prior constraints. However, different brain activation patterns may produce similar p...
Signal Processing, Computer-Assisted Brain Mapping Electroencephalography Neural Networks, Computer Algorithms Computer Simulation Convolutional Neural Networks Brain Humans
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Resource-Efficient Continual Learning for Personalized Online Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time...
Humans Machine Learning Electroencephalography Seizures Signal Processing, Computer-Assisted Algorithms Deep Learning
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Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnostic biomarkers of SZ using explainable machine learning methods. However, the generalizability of ide...
Schizophrenia Deep Learning Electroencephalography Signal Processing, Computer-Assisted Humans Biomarkers
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The Impact of Cross-Validation Schemes for EEG-Based Auditory Attention Detection with Deep Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
This study assesses the performance of different cross-validation splits for brain-signal-based Auditory Attention Decoding (AAD) using deep neural networks on three publicly available Electroencephalography datasets. We investigate the effect of tri...
Signal Processing, Computer-Assisted Attention Deep Learning Electroencephalography Humans Brain Reproducibility of Results Neural Networks, Computer
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Enhancing sleep stage classification with 2-class stratification and permutation-based channel selection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
We present a method that uses a convolutional neural network (CNN) called EEGNeX to extract and classify the characteristics of sleep-related waveforms from electroencephalographic (EEG) signals in different stages of sleep. Our results showed that t...
Humans Electroencephalography Signal Processing, Computer-Assisted Sleep Stages Algorithms ROC Curve Neural Networks, Computer
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