Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

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EEG-Based Emotion Recognition with Combined Fuzzy Inference via Integrating Weighted Fuzzy Rule Inference and Interpolation

Mathematics, Год журнала: 2025, Номер 13(1), С. 166 - 166

Опубликована: Янв. 5, 2025

Emotions play a significant role in shaping psychological activities, behaviour, and interpersonal communication. Reflecting this importance, automated emotion classification has become vital research area artificial intelligence. Electroencephalogram (EEG)-based recognition is particularly promising due to its high temporal resolution resistance manipulation. This study introduces an advanced fuzzy inference algorithm for EEG data-driven recognition, effectively addressing the ambiguity of emotional states. By combining adaptive rule generation, feature evaluation, weighted interpolation, proposed approach achieves accurate while handling incomplete knowledge. Experimental results demonstrate that integrated system outperforms state-of-the-art techniques, offering improved accuracy robustness under uncertainty.

Язык: Английский

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Develop an emotion recognition system using jointly connectivity between electroencephalogram and electrocardiogram signals

Heliyon, Год журнала: 2025, Номер 11(2), С. e41767 - e41767

Опубликована: Янв. 1, 2025

Язык: Английский

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ERTNet: an interpretable transformer-based framework for EEG emotion recognition

Frontiers in Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Янв. 17, 2024

Background Emotion recognition using EEG signals enables clinicians to assess patients’ emotional states with precision and immediacy. However, the complexity of signal data poses challenges for traditional methods. Deep learning techniques effectively capture nuanced cues within these by leveraging extensive data. Nonetheless, most deep lack interpretability while maintaining accuracy. Methods We developed an interpretable end-to-end emotion framework rooted in hybrid CNN transformer architecture. Specifically, temporal convolution isolates salient information from filtering out potential high-frequency noise. Spatial discerns topological connections between channels. Subsequently, module processes feature maps integrate high-level spatiotemporal features, enabling identification prevailing state. Results Experiments’ results demonstrated that our model excels diverse classification, achieving accuracy 74.23% ± 2.59% on dimensional (DEAP) 67.17% 1.70% discrete (SEED-V). These surpass performances both LSTM-based counterparts. Through interpretive analysis, we ascertained beta gamma bands exert significant impact performance. Notably, can independently tailor a Gaussian-like kernel, noise input Discussion Given its robust performance interpretative capabilities, proposed is promising tool EEG-driven brain-computer interface.

Язык: Английский

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EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN

Sensors, Год журнала: 2024, Номер 24(6), С. 1889 - 1889

Опубликована: Март 15, 2024

Analysis of brain signals is essential to the study mental states and various neurological conditions. The two most prevalent noninvasive for measuring activities are electroencephalography (EEG) functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number channels, provides richer spatial information. Although few previous studies have explored use multimodal deep-learning models analyze activity both EEG subject-independent training–testing split analysis remains underexplored. results setting directly show model’s ability on unseen subjects, which crucial real-world applications. In this paper, we introduce EF-Net, new CNN-based model. We evaluate EF-Net an EEG-fNIRS word generation (WG) dataset state recognition task, primarily focusing setting. For completeness, report in subject-dependent subject-semidependent settings as well. compare our model five baseline approaches, including three traditional machine learning methods deep methods. demonstrates superior performance accuracy F1 score, surpassing these baselines. Our achieves scores 99.36%, 98.31%, 65.05% subject-dependent, subject-semidependent, settings, respectively, best 1.83%, 4.34%, 2.13% These highlight EF-Net’s capability effectively learn interpret across different subjects.

Язык: Английский

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Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses

Diagnostics, Год журнала: 2025, Номер 15(4), С. 434 - 434

Опубликована: Фев. 11, 2025

Artificial intelligence (AI) has emerged as a transformative force in psychiatry, improving diagnostic precision, treatment personalization, and early intervention through advanced data analysis techniques. This review explores recent advancements AI applications within focusing on EEG ECG analysis, speech natural language processing (NLP), blood biomarker integration, social media utilization. EEG-based models have significantly enhanced the detection of disorders such depression schizophrenia spectral connectivity analyses. ECG-based approaches provided insights into emotional regulation stress-related conditions using heart rate variability. Speech frameworks, leveraging large (LLMs), improved cognitive impairments psychiatric symptoms nuanced linguistic feature extraction. Meanwhile, analyses deepened our understanding molecular underpinnings mental health disorders, analytics demonstrated potential for real-time surveillance. Despite these advancements, challenges heterogeneity, interpretability, ethical considerations remain barriers to widespread clinical adoption. Future research must prioritize development explainable models, regulatory compliance, integration diverse datasets maximize impact care.

Язык: Английский

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A comprehensive exploration of motion sickness process analysis from EEG signal and virtual reality

Computer Methods and Programs in Biomedicine, Год журнала: 2025, Номер 264, С. 108714 - 108714

Опубликована: Март 7, 2025

Язык: Английский

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Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning

Computers in Biology and Medicine, Год журнала: 2023, Номер 169, С. 107894 - 107894

Опубликована: Дек. 22, 2023

Язык: Английский

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Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Май 9, 2024

Abstract The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests methods learning identifying diagnostic biomarkers using three prominent deep network models: BiLSTM, reservoir SNN, NeuCube. EEG data from datasets related to epilepsy, migraine, healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while SNN activities NeuCube dynamics identify channels as biomarkers. achieve 90 85% classification accuracy, achieves 97%, all pinpointing potential like T6, F7, C4, F8. research bears implications refining classification, analysis, early brain state diagnosis, enhancing AI models with interpretability discovery. proposed techniques hold promise streamlined brain-computer interfaces clinical applications, representing significant advancement in pattern discovery across the most popular addressing crucial problem. Further is planned how can these predict an onset of states.

Язык: Английский

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A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e2065 - e2065

Опубликована: Май 23, 2024

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human–computer interaction. In recent years, with the relentless advancement deep learning techniques, using for analyzing assumed prominent role in emotion recognition. Applying context EEG-based carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they yet to undergo comprehensive precise classification summarization process. The existing classifications are somewhat coarse, insufficient attention given potential applications within domain. Therefore, article systematically classifies developments recognition, providing researchers lucid understanding field’s various trajectories methodologies. Additionally, it elucidates why distinct directions necessitate modeling approaches. conclusion, synthesizes dissects significance emphasizing its promising avenues future application.

Язык: Английский

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