A systematic review on automated human emotion recognition using electroencephalogram signals and artificial intelligence

Results in Engineering, Journal Year: 2023, Volume and Issue: 18, P. 101027 - 101027

Published: March 17, 2023

Language: Английский

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A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions

Healthcare Analytics, Journal Year: 2024, Volume and Issue: 5, P. 100340 - 100340

Published: May 8, 2024

Data augmentation involves artificially expanding a dataset by applying various transformations to the existing data. Recent developments in deep learning have advanced data augmentation, enabling more complex transformations. Especially vital medical domain, learning-based improves model robustness generating realistic variations images, enhancing diagnostic and predictive task performance. Therefore, assist researchers experts their pursuits, there is need for an extensive informative study that covers latest advancements growing domain of imaging. There gap literature regarding recent augmentation. This explores diverse applications imaging analyzes research these areas address this gap. The also popular datasets evaluation metrics improve understanding. Subsequently, provides short discussion conventional techniques along with detailed on algorithms further results experimental details from state-of-the-art understand progress Finally, discusses challenges proposes future directions concerns. systematic review offers thorough overview imaging, covering application domains, models, analysis, challenges, directions. It valuable resource multidisciplinary studies making decisions based analytics.

Language: Английский

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TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102575 - 102575

Published: May 20, 2024

Language: Английский

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An enhanced ensemble deep random vector functional link network for driver fatigue recognition

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 123, P. 106237 - 106237

Published: April 8, 2023

This work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against low feature learning capability edRVFL from raw EEG signals, two strategies were exploited in this work. Specifically, first one was to exploit advantages extractor module CNNs, i.e., CNN features as input network. The second improve An enhanced edRFVL named FGloWD-edRVFL proposed, which four enhancements implemented, including forest-based Feature selection, Global output layer, Weighting and entropy-based Dynamic ensemble. proposed evaluated on challenging cross-subject recognition tasks. results indicated that model could boost performance, significantly outperforming all strong baselines. step-wise analysis further demonstrated effectiveness

Language: Английский

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LGNet: Learning local–global EEG representations for cognitive workload classification in simulated flights

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 92, P. 106046 - 106046

Published: Feb. 7, 2024

Language: Английский

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Data Augmentation of SSVEPs Using Source Aliasing Matrix Estimation for Brain–Computer Interfaces

IEEE Transactions on Biomedical Engineering, Journal Year: 2022, Volume and Issue: 70(6), P. 1775 - 1785

Published: Dec. 7, 2022

Currently, ensemble task-related component analysis (eTRCA) and task discriminative (TDCA) are the state-of-the-art algorithms for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, training BCIs requires multiple calibration trials. With insufficient data, accuracy of BCI will degrade, or even become invalid with only one trial. collecting a large amount electroencephalography (EEG) data is time-consuming laborious process, which hinders practical use eTRCA TDCA.This study proposed novel method, namely Source Aliasing Matrix Estimation (SAME), to augment SSVEP-BCIs. SAME could generate artificial EEG trials featured SSVEPs. Its effectiveness was evaluated using two public datasets (i.e., Benchmark, BETA).When combined SAME, both TDCA had significantly improved performance limited number data. Specifically, increased average by about 12% 3%, respectively, as few Notably, enabled work well single trial, achieving an >90% Benchmark dataset >70% BETA 1-second EEG.SAME effective method SSVEP-BCIs thereby enhancing TDCA.We propose new data-augmentation that compatible SSVEP-based BCIs. It can reduce efforts required calibrate SSVEP-BCIs, promising development

Language: Английский

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Cross-Stimulus Transfer Method Using Common Impulse Response for Fast Calibration of SSVEP-Based BCIs

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2024, Volume and Issue: 73, P. 1 - 14

Published: Jan. 1, 2024

To achieve a high information transfer rate (ITR) in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), current decoding methods require extensive calibration efforts to train the model parameters for each stimulus. facilitate process, this study proposed cross-stimulus method, which learns common spatial filter and impulse response from few source stimuli then transfers them new target stimulus SSVEP feature extraction. First, are obtained by minimizing deviation between spatially filtered SSVEPs constructed templates. Then, vector comprised of two correlation coefficients is utilized recognition, one coefficient templates, other canonical reference signals. For performance evaluation, recognition method was compared with state-of-art on public datasets self-collected dataset. Results showed that can obtain higher fewer training blocks, demonstrating has capability fast SSVEP-based BCIs.

Language: Английский

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Deep learning in motor imagery EEG signal decoding: A Systematic Review

Neurocomputing, Journal Year: 2024, Volume and Issue: 610, P. 128577 - 128577

Published: Sept. 14, 2024

Language: Английский

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Low-Shot Machine Learning for Medical Image Classification

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 26 - 53

Published: Jan. 1, 2025

Language: Английский

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IoT-Cloud-Centric Smart Healthcare Monitoring System for Heart Disease Prediction Using a Gated-Controlled Deep Unfolding Network with Crayfish Optimization

International Journal of Computational Intelligence and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

The rising incidence of heart disease requires effective and robust prediction algorithms, especially in Internet Things (IoT)-cloud-based smart healthcare frameworks. This study presents a novel method for forecasting cardiovascular using superior data preprocessing, feature selection, deep learning techniques. First, preprocessing is done the Z-score min–max normalization technique to ensure consistent scaling standardize dataset. After an innovative hybrid selection that combines Black Widow Optimization (BWO) Influencer Buddy (IBO) utilized. By achieving equilibrium between invention execution, BWO-IBO enhances extracts most pertinent information prediction. Gates-Controlled Deep Unfolding Network (GCDUN), which based on Crayfish Algorithm (COA), framework Through use gates-controlled mechanism COA component speeds up network parameter tuning crayfish behavior, GCDUN-COA increases representation decision plane. fusion IoT cloud-based takes present collection, processing, remote monitoring notch higher, thus making system highly scalable efficient clinical use. When predicting cardiac disease, recommended shows improved F1-score, specificity, accuracy, recall, precision continuously above 99% across all performance metrics. providing prompt diagnosis intervention via intelligent, adaptive system, IoT-driven medical technology has potential revolutionize care.

Language: Английский

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