Classification of EEG signals based on CNN-Transformer model DOI
Jianwei Liu, Enzeng Dong, Jigang Tong

et al.

2022 IEEE International Conference on Mechatronics and Automation (ICMA), Journal Year: 2023, Volume and Issue: unknown, P. 2095 - 2099

Published: Aug. 6, 2023

Brain-computer interfaces (BCI) based on EEG have attracted extensive research and attention worldwide, while motor imagery (MI), mental arithmetic (MA), P300 event-related potentials are a few of the more commonly used paradigms.Vision Transformer(ViT) is new Transformer model that has superior global processing power compared to Convolutional Neural Networks (CNN) Recurrent (RNN).In this study, we propose hybrid CNN-Transformer uses CNN convolve signals in time space, followed by ViT for processing, finally optimizes using 10-run $\times 10$-fold cross-validation validates it publicly available dataset 29 subjects. Final accuracies 87.23% 90.79% were achieved MI MA tasks, respectively. Compared other literature, higher classification accuracies.

Language: Английский

Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications DOI Creative Commons
Berdakh Abibullaev, Aigerim Keutayeva, Amin Zollanvari

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 127271 - 127301

Published: Jan. 1, 2023

Brain-computer interfaces (BCIs) have undergone significant advancements in recent years. The integration of deep learning techniques, specifically transformers, has shown promising development research and application domains. Transformers, which were originally designed for natural language processing, now made notable inroads into BCIs, offering a unique self-attention mechanism that adeptly handles the temporal dynamics brain signals. This comprehensive survey delves transformers providing readers with lucid understanding their foundational principles, inherent advantages, potential challenges, diverse applications. In addition to discussing benefits we also address limitations, such as computational overhead, interpretability concerns, data-intensive nature these models, well-rounded analysis. Furthermore, paper sheds light on myriad BCI applications benefited from incorporation transformers. These span motor imagery decoding, emotion recognition, sleep stage analysis novel ventures speech reconstruction. review serves holistic guide researchers practitioners, panoramic view transformative landscape. With inclusion examples references, will gain deeper topic its significance field.

Language: Английский

Citations

29
Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks DOI

Md. Hasin Raihan Rabbani,

Sheikh Md. Rabiul Islam

Cognitive Neurodynamics, Journal Year: 2023, Volume and Issue: 18(4), P. 1489 - 1506

Published: June 30, 2023

Language: Английский

Citations

16
Decoding emotions through personalized multi-modal fNIRS-EEG Systems: Exploring deterministic fusion techniques DOI Creative Commons
Alireza Farrokhi Nia,

Vanessa Tang,

Gonzalo D. Maso Talou

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107632 - 107632

Published: Feb. 12, 2025

Language: Английский

Citations

0
STA-Net: Spatial–temporal alignment network for hybrid EEG-fNIRS decoding DOI
Mutian Liu, Banghua Yang, Lin Meng

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103023 - 103023

Published: Feb. 1, 2025

Language: Английский

Citations

0
Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging DOI Creative Commons
Chayut Bunterngchit, Jiaxing Wang, Zeng‐Guang Hou

et al.

IEEE Journal of Translational Engineering in Health and Medicine, Journal Year: 2024, Volume and Issue: 12, P. 600 - 612

Published: Jan. 1, 2024

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement brain-computer interfaces (BCIs). However, existing research in this domain has grappled with challenge efficient selection features, resulting underutilization temporal richness EEG spatial specificity fNIRS data.To effectively address challenge, study proposed a deep learning architecture called multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) data images, leveraging advanced feature extraction techniques. transformed into 2D images using short-time Fourier transform, applied transfer to extract discriminative consequently integrated them fNIRS-derived spectral entropy features. This approach aimed bridge gaps EEG-fNIRS-based BCI by enhancing classification accuracy versatility across various cognitive motor imagery tasks.Experimental results two public datasets demonstrated superiority our over state-of-the-art methods.Thus, high precise utilization MDNF demonstrates potential clinical applications for neurodiagnostics rehabilitation, thereby paving method patient-specific therapeutic strategies.

Language: Английский

Citations

3
OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS–Brain Computer Interface DOI Creative Commons
Muhammad Umair Ali,

Kwang Su Kim,

Karam Dad Kallu

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(5), P. 608 - 608

Published: May 18, 2023

Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed optimization-based feature selection algorithm systematically investigate nature multimodal fused features. After preprocessing acquired both (i.e., EEG fNIRS), temporal statistical features were computed separately with a 10 s interval for each modality. The create training vector. A wrapper-based binary enhanced whale optimization (E-WOA) was used select optimal/efficient subset using support-vector-machine-based cost function. An online dataset 29 healthy individuals evaluate performance proposed methodology. findings suggest that approach enhances classification evaluating degree complementarity between characteristics selecting most efficient subset. E-WOA showed high rate (94.22 ± 5.39%). exhibited 3.85% increase compared conventional algorithm. hybrid framework outperformed traditional (p < 0.01). These indicate potential efficacy several neuroclinical applications.

Language: Английский

Citations

7
Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces DOI Creative Commons
Qiwei Xue, Yuntao Song, Huapeng Wu

et al.

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: March 28, 2024

Introduction Within the development of brain-computer interface (BCI) systems, it is crucial to consider impact brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect topological relationship among electrodes, thereby hindering effective decoding activity. Methods Inspired by concept neuronal forward-forward (F-F) mechanism, a novel DL framework based Graph Neural Network combined mechanism (F-FGCN) presented. F-FGCN aims enhance EEG performance applying functional relationships propagation mechanism. The fusion process involves converting multi-channel into sequence signals constructing grounded Pearson correlation coeffcient, effectively representing associations between channels. Our model initially pre-trains Convolutional (GCN), fine-tunes output layer obtain feature vector. Moreover, F-F used for advanced extraction classification. Results discussion Achievement assessed PhysioNet dataset four-class categorization, compared with various classical state-of-the-art models. learned features substantially amplify downstream classifiers, achieving highest accuracy 96.11% 82.37% at subject group levels, respectively. Experimental results affirm potency FFGCN in enhancing performance, thus paving way BCI applications.

Language: Английский

Citations

2
E-FNet: A EEG-fNIRS dual-stream model for Brain–Computer Interfaces DOI
B. X. Yu, Lei Cao, Jie Jia

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 106943 - 106943

Published: Sept. 30, 2024

Language: Английский

Citations

2
Cybersecurity in neural interfaces: Survey and future trends DOI Open Access
Xinyu Jiang, Jiahao Fan, Ziyue Zhu

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 167, P. 107604 - 107604

Published: Oct. 20, 2023

Language: Английский

Citations

6
Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs DOI Open Access
Shouwei Gao, Kang Zhou, Jun Zhang

et al.

Healthcare, Journal Year: 2023, Volume and Issue: 11(7), P. 1014 - 1014

Published: April 2, 2023

As a widely used brain-computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, tolerance for artifacts, and robust performance across diverse users. However, incidence mental fatigue from prolonged, repetitive stimulation is critical issue SSVEP-based BCIs. Music often as convenient, non-invasive means relieving fatigue. This study investigates compensatory effect music on through introduction different modes background in long-duration, SSVEP-BCI tasks. Changes electroencephalography power index, SSVEP amplitude, signal-to-noise ratio were to assess participants' The study's results show that exciting task was effective In addition, continuous tasks, combination musical soothing during rest interval phase proved more reducing users' suggests can provide practical solution long-duration BCI implementation.

Language: Английский

Citations

5