Autonomous system for EEG-based multiple abnormal mental states classification using hybrid deep neural networks under flight environment
Detection of the pilots' mental states is particularly critical because their abnormal mental
states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility …
states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility …
Deep metric learning with locality sensitive mining for self-correcting source separation of neural spiking signals
Automated source separation algorithms have become a central tool in neuroengineering
and neuroscience, where they are used to decompose neurophysiological signal into its …
and neuroscience, where they are used to decompose neurophysiological signal into its …
Speech imagery decoding using EEG signals and deep learning: A survey
Speech imagery-based Brain-computer interface (BCI) using Electroencephalogram (EEG)
signal is a promising area of research for individuals with severe speech production …
signal is a promising area of research for individuals with severe speech production …
Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review
N Rahman, DM Khan, K Masroor, M Arshad… - Cognitive …, 2024 - Springer
Numerous individuals encounter challenges in verbal communication due to various factors,
including physical disabilities, neurological disorders, and strokes. In response to this …
including physical disabilities, neurological disorders, and strokes. In response to this …
[HTML][HTML] Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study
Background Cardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical
research has shown that early identification of CA reduces mortality. Algorithms capable of …
research has shown that early identification of CA reduces mortality. Algorithms capable of …
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
The global prevalence of childhood and adolescent obesity is a major concern due to its
association with chronic diseases and long-term health risks. Artificial intelligence …
association with chronic diseases and long-term health risks. Artificial intelligence …
[HTML][HTML] ChineseEEG: A Chinese linguistic corpora EEG dataset for semantic alignment and neural decoding
An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the
understanding of how the brain encodes semantic information and contribute to semantic …
understanding of how the brain encodes semantic information and contribute to semantic …
MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response
to external events and their associated underlying complex spatiotemporal feature …
to external events and their associated underlying complex spatiotemporal feature …
Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications
Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record
discrete signals across multiple scales, but how to effectively process and analyze these …
discrete signals across multiple scales, but how to effectively process and analyze these …
[HTML][HTML] Progress in Non-Invasive Cognitive Brain-Computer Interface and Implications for Mind-Uploading
IWAD Astawa, HD Purnomo, I Sembiring - International Journal of Artificial …, 2024 - ijair.id
Mind-uploading, the vision of transferring human consciousness into a digital realm, relies
on a profound comprehension of the brain and cutting-edge technology. Non-invasive …
on a profound comprehension of the brain and cutting-edge technology. Non-invasive …