Generalizing to unseen domains: A survey on domain generalization
Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …
the same. To this end, a key requirement is to develop models that can generalize to unseen …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
[HTML][HTML] EEG-based methods for recovery prognosis of patients with disorders of consciousness: a systematic review
Abstract Objective Disorders of consciousness (DoC) are acquired conditions of severely
altered consciousness. Electroencephalography (EEG)-derived biomarkers have been …
altered consciousness. Electroencephalography (EEG)-derived biomarkers have been …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
Epileptic eeg classification by using time-frequency images for deep learning
Epilepsy is one of the most common brain disorders worldwide. The most frequently used
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
A novel EEG-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism
Z Zhang, Q Meng, LC Jin, H Wang, H Hou - Expert Systems with …, 2024 - Elsevier
Electroencephalography (EEG) is capable of capturing the evocative neural information
within the brain. As a result, it has been increasingly used for identifying neurological …
within the brain. As a result, it has been increasingly used for identifying neurological …
Time–frequency signal processing: Today and future
Most real-life signals exhibit non-stationary characteristics. Processing of such signals
separately in the time-domain or in the frequency-domain does not provide sufficient …
separately in the time-domain or in the frequency-domain does not provide sufficient …
Ppi: Pretraining brain signal model for patient-independent seizure detection
Automated seizure detection is of great importance to epilepsy diagnosis and treatment. An
emerging method used in seizure detection, stereoelectroencephalography (SEEG), can …
emerging method used in seizure detection, stereoelectroencephalography (SEEG), can …
Six-center assessment of CNN-Transformer with belief matching loss for patient-independent seizure detection in EEG
Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by
visual inspection. This process is often time-consuming, especially for EEG recordings that …
visual inspection. This process is often time-consuming, especially for EEG recordings that …
Deep long short term memory based minimum variance kernel random vector functional link network for epileptic EEG signal classification
In this paper, the efficiently extracted and reduced features using deep long short-term
memory (DLSTM) of the epileptic EEG signal integrated with minimum variance kernel …
memory (DLSTM) of the epileptic EEG signal integrated with minimum variance kernel …