Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques DOI Creative Commons

M. Karthiga,

E. Suganya,

S. Sountharrajan

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 4, 2024

Language: Английский

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

Javid Farhadi Sedehi,

Nader Jafarnia Dabanloo, Keivan Maghooli

et al.

Heliyon, Journal Year: 2025, Volume and Issue: 11(2), P. e41767 - e41767

Published: Jan. 1, 2025

Language: Английский

Citations

3
Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis DOI
Shraddha Jain, Ruchi Srivastava

Brain Topography, Journal Year: 2025, Volume and Issue: 38(3)

Published: Feb. 24, 2025

Language: Английский

Citations

3
Application of Transfer Learning for Biomedical Signals: A Comprehensive Review of the Last Decade (2014-2024) DOI Creative Commons
Mahboobeh Jafari, Xiaohui Tao, Prabal Datta Barua

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: 118, P. 102982 - 102982

Published: Jan. 30, 2025

Language: Английский

Citations

1
EEG emotion recognition based on efficient-capsule network with convolutional attention DOI
Wei Tang, LongQing Fan, Xuefen Lin

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107473 - 107473

Published: Jan. 5, 2025

Language: Английский

Citations

0
DDNet: a hybrid network based on deep adaptive multi-head attention and dynamic graph convolution for EEG emotion recognition DOI

Bingyue Xu,

Xin Zhang, Xiu Zhang

et al.

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(4)

Published: Feb. 14, 2025

Language: Английский

Citations

0
Electroencephalogram Based Emotion Recognition Using Hybrid Intelligent Method and Discrete Wavelet Transform DOI Creative Commons
Duy Nguyen, M.T. Nguyen, Kou Yamada

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2328 - 2328

Published: Feb. 21, 2025

Electroencephalography-based emotion recognition is essential for brain-computer interface combined with artificial intelligence. This paper proposes a novel algorithm human detection using hybrid paradigm of convolutional neural networks and boosting model. The proposed employs two subsets 18 14 features extracted from four sub-bands discrete wavelet transform. These are identified as the optimal most relevant, among 42 original input 8 6 productive channels dual genetic wise-subject 5-fold cross validation procedure in which first second algorithms address efficient feature subsets. estimated by differently intelligent models on set. produces an accuracy 70.43%/76.05%, precision 69.88%/74.57%, recall 98.70%/99.17%, F1 score 81.83%/85.13% valence/arousal classifications, suggest that frontal left regions cortex associate especially to emotions.

Language: Английский

Citations

0
Multiclass classification of epileptic seizure phases using a novel HFO-based feature extraction model DOI

Pelin Sari Tekten,

Soner Kotan,

Fırat Kaçar

et al.

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(4)

Published: Feb. 22, 2025

Language: Английский

Citations

0
EEG-based emotion recognition model using fuzzy adjacency matrix combined with convolutional multi-head graph attention mechanism DOI
Mingwei Cao, Yindong Dong, Deli Chen

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

Citations

0
A Novel Fusion Framework Combining Graph Embedding Class-Based Convolutional Recurrent Attention Network with Brown Bear Optimization Algorithm for EEG-Based Parkinson’s Disease Recognition DOI
N Shirisha,

Baranitharan Kannan,

Padmanaban Kuppan

et al.

Journal of Molecular Neuroscience, Journal Year: 2025, Volume and Issue: 75(1)

Published: March 15, 2025

Language: Английский

Citations

0
Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis DOI Creative Commons

T. Thamaraimanalan,

Dhanalakshmi Gopal,

S. Vignesh

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 16, 2025

The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these remains a challenge due to their inherent complexity non-linearity. This study introduces novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) Adaptive Neuro-Fuzzy Inference Systems (ANFIS), enhance pattern recognition in multimodal signal analysis. PCA reduces the dimensionality EEG data while retaining salient features, enabling computational efficiency. ANFIS combines adaptability neural networks with interpretability fuzzy logic, making it well-suited model non-linear relationships within signals. Performance metrics our proposed such as accuracy, sensitivity, These additions highlight effectiveness method provide concise summary findings. achieves superior classification performance, an unprecedented accuracy 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using diverse dataset, demonstrating method's robustness sensitivity. integration addresses key challenges analysis, artifact contamination non-stationarity, ensuring reliable feature extraction classification. research has significant implications for both neuroscience clinical practice. By advancing understanding processes, PCA-ANFIS facilitates accurate diagnosis treatment disorders neurological conditions. Future work will focus on testing approach larger more datasets exploring its applicability domains neurofeedback, neuromarketing, brain-computer interfaces. establishes capable tool precise efficient processing.

Language: Английский

Citations

0