[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …
human–system/robot interactions have been actively explored, especially in brain robotics …
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
A deep learning based ensemble learning method for epileptic seizure prediction
In epilepsy, patients suffer from seizures which cannot be controlled with medicines or
surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is …
surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is …
Spatio-temporal MLP network for seizure prediction using EEG signals
In this paper, we propose an end-to-end epilepsy seizure prediction method based on multi-
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
Sparse spectrum based swarm decomposition for robust nonstationary signal analysis with application to sleep apnea detection from EEG
SV Bhalerao, RB Pachori - Biomedical Signal Processing and Control, 2022 - Elsevier
Background and motivation Time–frequency representation (TFR) of a signal finds its
application in numerous fields for non-stationary multicomponent signal analysis. Due to …
application in numerous fields for non-stationary multicomponent signal analysis. Due to …
An overview of EEG-based machine learning methods in seizure prediction and opportunities for neurologists in this field
B Maimaiti, H Meng, Y Lv, J Qiu, Z Zhu, Y Xie, Y Li… - Neuroscience, 2022 - Elsevier
The unpredictability of epileptic seizures is one of the most problematic aspects of the field of
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …
[HTML][HTML] Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models
The development of seizure prediction models is often based on long-term scalp
electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive …
electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive …
Automatic detection of schizophrenia based on spatial–temporal feature mapping and LeViT with EEG signals
B Li, J Wang, Z Guo, Y Li - Expert Systems with Applications, 2023 - Elsevier
Electroencephalography (EEG) signals, which record brain activity, are known to be very
useful in diagnosing brain-related conditions. However, manual examination of these EEG …
useful in diagnosing brain-related conditions. However, manual examination of these EEG …
ForeSeiz: An IoMT based headband for Real-time epileptic seizure forecasting
BP Prathaban, R Balasubramanian… - Expert Systems with …, 2022 - Elsevier
According to the 2020′ s scientific reports of World Health Organization (WHO), around 70
million people around the world are affected by Epilepsy. Around 15% of the deaths in …
million people around the world are affected by Epilepsy. Around 15% of the deaths in …
3D residual-attention-deep-network-based childhood epilepsy syndrome classification
Interictal electroencephalograms (EEGs) usually contain important information for epilepsy
analysis and diagnosis. However, the focus of existing research has mainly been on …
analysis and diagnosis. However, the focus of existing research has mainly been on …