Automated detection and forecasting of covid-19 using deep learning techniques: A review
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning
In the rapidly advancing field of biomedical engineering, effective real-time control of
artificial limbs is a pressing research concern. Addressing this, the current study introduces a …
artificial limbs is a pressing research concern. Addressing this, the current study introduces a …
A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications
W Ma, Y Zheng, T Li, Z Li, Y Li, L Wang - PeerJ Computer Science, 2024 - peerj.com
Emotion recognition utilizing EEG signals has emerged as a pivotal component of human–
computer interaction. In recent years, with the relentless advancement of deep learning …
computer interaction. In recent years, with the relentless advancement of deep learning …
EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN
Analysis of brain signals is essential to the study of mental states and various neurological
conditions. The two most prevalent noninvasive signals for measuring brain activities are …
conditions. The two most prevalent noninvasive signals for measuring brain activities are …
Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine
The study introduces a new online spike encoding algorithm for spiking neural networks
(SNN) and suggests new methods for learning and identifying diagnostic biomarkers using …
(SNN) and suggests new methods for learning and identifying diagnostic biomarkers using …
Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning
XS Shi, Q She, F Fang, M Meng, T Tan… - Computers in Biology and …, 2024 - Elsevier
Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain
adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL …
adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL …
[HTML][HTML] Analyzing emotions in online classes: Unveiling insights through topic modeling, statistical analysis, and random walk techniques
High dropout rates globally perpetuate educational disparities with various underlying
causes. Despite numerous strategies to address this issue, more attention should be given …
causes. Despite numerous strategies to address this issue, more attention should be given …
ERTNet: an interpretable transformer-based framework for EEG emotion recognition
R Liu, Y Chao, X Ma, X Sha, L Sun, S Li… - Frontiers in …, 2024 - frontiersin.org
Background Emotion recognition using EEG signals enables clinicians to assess patients'
emotional states with precision and immediacy. However, the complexity of EEG signal data …
emotional states with precision and immediacy. However, the complexity of EEG signal data …
Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
Ambient Intelligence (AMI) represents a significant advancement in information technology
that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise …
that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise …
Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
Medical time series data, such as Electroencephalography (EEG) and Electrocardiography
(ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases …
(ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases …