A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification
The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …
correlation in the long-sequence data and visualizing the model. As time-series data, the …
Learning topology-agnostic eeg representations with geometry-aware modeling
Large-scale pre-training has shown great potential to enhance models on downstream tasks
in vision and language. Developing similar techniques for scalp electroencephalogram …
in vision and language. Developing similar techniques for scalp electroencephalogram …
Cross-subject EEG emotion recognition with self-organized graph neural network
As a physiological process and high-level cognitive behavior, emotion is an important
subarea in neuroscience research. Emotion recognition across subjects based on brain …
subarea in neuroscience research. Emotion recognition across subjects based on brain …
Eeg-gcnn: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network
N Wagh, Y Varatharajah - Machine Learning for Health, 2020 - proceedings.mlr.press
This paper presents a novel graph convolutional neural network (GCNN)-based approach
for improving the diagnosis of neurological diseases using scalp-electroencephalograms …
for improving the diagnosis of neurological diseases using scalp-electroencephalograms …
Classification of EEG signals using Transformer based deep learning and ensemble models
Abstract A Brain-Computer Interface (BCI) is a communication and control system designed
to provide interaction between a user and a computer device. This interaction is based on …
to provide interaction between a user and a computer device. This interaction is based on …
A Review of Graph Theory-Based Diagnosis of Neurological Disorders Based on EEG and MRI
Graph theory analysis, as a mathematical tool, has been widely employed in studying the
connectivity of the brain to explore the structural organization. Through the computation of …
connectivity of the brain to explore the structural organization. Through the computation of …
Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy
Y Varatharajah, B Berry, J Cimbalnik… - Journal of neural …, 2018 - iopscience.iop.org
Objective. An ability to map seizure-generating brain tissue, ie the seizure onset zone (SOZ),
without recording actual seizures could reduce the duration of invasive EEG monitoring for …
without recording actual seizures could reduce the duration of invasive EEG monitoring for …
EEG-Oriented Self-Supervised Learning With Triple Information Pathways Network
Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have
attracted widespread attention for monitoring the clinical condition of users and identifying …
attracted widespread attention for monitoring the clinical condition of users and identifying …
Evaluating latent space robustness and uncertainty of EEG-ML models under realistic distribution shifts
The recent availability of large datasets in bio-medicine has inspired the development of
representation learning methods for multiple healthcare applications. Despite advances in …
representation learning methods for multiple healthcare applications. Despite advances in …
Multi-tier platform for cognizing massive electroencephalogram
An end-to-end platform assembling multiple tiers is built for precisely cognizing brain
activities. Being fed massive electroencephalogram (EEG) data, the time-frequency …
activities. Being fed massive electroencephalogram (EEG) data, the time-frequency …