CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals

Diagnostics, Год журнала: 2025, Номер 15(3), С. 363 - 363

Опубликована: Фев. 4, 2025

Background\Objectives: Solving the secrets of brain is a significant challenge for researchers. This work aims to contribute this area by presenting new explainable feature engineering (XFE) architecture designed obtain results related stress and mental performance using electroencephalography (EEG) signals. Materials Methods: Two EEG datasets were collected detect stress. To achieve classification results, XFE model was developed, incorporating novel extraction function called Cubic Pattern (CubicPat), which generates three-dimensional vector coding channels. Classification obtained cumulative weighted iterative neighborhood component analysis (CWINCA) selector t-algorithm-based k-nearest neighbors (tkNN) classifier. Additionally, generated CWINCA Directed Lobish (DLob). Results: The CubicPat-based demonstrated both interpretability. Using 10-fold cross-validation (CV) leave-one-subject-out (LOSO) CV, introduced CubicPat-driven achieved over 95% 75% accuracies, respectively, datasets. Conclusions: interpretable deploying DLob statistical analysis.

Язык: Английский

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Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method

Journal of Hydrology, Год журнала: 2024, Номер 642, С. 131867 - 131867

Опубликована: Авг. 23, 2024

Язык: Английский

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AtLSMMs network: An attentional-biLSTM based multi-model prediction for smartphone visual fatigue

Displays, Год журнала: 2024, Номер 84, С. 102754 - 102754

Опубликована: Май 29, 2024

Язык: Английский

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Interactive multi-agent convolutional broad learning system for EEG emotion recognition

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125420 - 125420

Опубликована: Сен. 1, 2024

Язык: Английский

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Understandable time frame-based biosignal processing

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 103, С. 107429 - 107429

Опубликована: Янв. 29, 2025

Язык: Английский

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TSMNet: A comprehensive network based on spatio-temporal representations for SSVEP classification

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 105, С. 107554 - 107554

Опубликована: Фев. 8, 2025

Язык: Английский

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Enhancing generic cognitive workload recognition with a dynamic hierarchical 3-D attention network and EEG features

Expert Systems with Applications, Год журнала: 2025, Номер 280, С. 127563 - 127563

Опубликована: Апрель 8, 2025

Язык: Английский

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A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition

Neural Networks, Год журнала: 2025, Номер unknown, С. 107457 - 107457

Опубликована: Апрель 1, 2025

Язык: Английский

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Diagnostic Accuracy of Machine Learning Algorithms in Electrocardiogram-Based Sleep Apnea Detection: A Systematic Review and Meta-Analysis

Sleep Medicine Reviews, Год журнала: 2025, Номер 81, С. 102097 - 102097

Опубликована: Май 7, 2025

Язык: Английский

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Emotion recognition based on convolutional gated recurrent units with attention

Connection Science, Год журнала: 2023, Номер 35(1)

Опубликована: Дек. 9, 2023

Studying brain activity and deciphering the information in electroencephalogram (EEG) signals has become an emerging research field, substantial advances have been made EEG-based classification of emotions. However, using different EEG features complementarity to discriminate other emotions is still challenging. Most existing models extract a single temporal feature from signal while ignoring crucial dynamic information, which, certain extent, constrains capability model. To address this issue, we propose Attention-Based Depthwise Parameterized Convolutional Gated Recurrent Unit (AB-DPCGRU) model validate it with mixed experiment on SEED SEED-IV datasets. The experimental outcomes revealed that accuracy outperforms state-of-the-art methods, which confirmed superiority our approach over currently popular emotion recognition models.

Язык: Английский

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