Biomedical Signal Processing and Control最新文献

{"title":"Design of an optimized rotation-invariant coordinate convolutional neural network driven medical IoT recommendation system integrating sentiment analysis for improved patient preference prediction","authors":"Rethina Kumar B ,&nbsp;P. Sudhakaran ,&nbsp;M. Baritha Begum ,&nbsp;S. Rajeswari","doi":"10.1016/j.bspc.2026.109742","DOIUrl":"10.1016/j.bspc.2026.109742","url":null,"abstract":"<div><div>Chronic and lifestyle-related diseases are rising globally, creating significant societal and economic burdens. To support effective long-term patient monitoring, an Optimized Rotation-Invariant Coordinate Convolutional Neural Network-driven Medical IoT Recommendation System integrating Sentiment Analysis for Improved Patient Preference Prediction (RICNN-IoT-SA-IPP) is proposed. The system collects multimodal data, including physiological and behavioural signals from IoT-based healthcare sensors and combines it with patient feedback sourced from electronic health records and medical consultation platforms. A Fast Guided Median Filter (FGMF) is employed to denoise and normalize the input, followed by spatial feature extraction utilizing Synchro-Transient-Extracting Transform (STET). These features are analyzed through a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN) to infer patient sentiment. A Rotation-Invariant Coordinate Convolutional Neural Network (RICNN) then performs preference prediction. To enhance prediction accuracy, the Levy Pelican Optimization Algorithm (LPOA) is used for optimizing feature weights and model parameters. The system performance is evaluated using Accuracy, Precision, Recall, F1-Score, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Computational Time. The proposed RICNN-IoT-SA-IPP model achieved 99.32% accuracy and 98.34% precision, while maintaining low error rates with MAE = 0.0855 and MSE = 0.0864, respectively. When compared with existing models, these outcomes represent an improvement of approximately 3–5% in classification metrics and a significant reduction in prediction error. This demonstrates that the proposed framework provides highly accurate, reliable, and computationally efficient patient preference predictions.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109742"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spike sequences classification for dengue and Zika infections in mosquito neurons using deep pre-trained models","authors":"Danial Sharifrazi ,&nbsp;Nouman Javed ,&nbsp;Roohallah Alizadehsani ,&nbsp;Prasad N. Paradkar ,&nbsp;U.Rajendra Acharya ,&nbsp;Asim Bhatti","doi":"10.1016/j.bspc.2026.109748","DOIUrl":"10.1016/j.bspc.2026.109748","url":null,"abstract":"<div><div>Mosquito-borne diseases are severe hazards to the health of both animals and humans. <em>Aedes aegypti</em> mosquitos are the primary vectors of several medically significant diseases, including dengue and Zika. Therefore, a thorough understanding of the neurons of mosquitos transmitting these diseases can be extremely beneficial in disease prevention. We hope to better comprehend the unique pattern found in considerable values of signal retrieved from mosquito neurons, known as spikes. There is currently no open-source neural spike sequence classification technique for mosquitos. To obtain outstanding outcomes, we demonstrate how to extract and classify spikes from mosquito neuron inputs using transfer learning approaches. Consequently, we highlight the role of deep pre-trained models that were trained using ImageNet weights.</div><div>The proposed methodology uses electrical spiking activity data from mosquito neurons collected with microelectrode array technology. To assess the method’s performance, data from 0, 1, 2, 3, and 7 days post-infection, reaching more than 15 million samples, were used. In this study, we also look at the influence of days post-infection on recognizing spikes in mosquito neurons.</div><div>Overall, we attempted for the first time to analyze the distinctive pattern in the spike sequence of mosquito neurons using Artificial Intelligence (AI) approaches and to determine the impact of these spikes over time.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109748"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNN–LSTM based deep learning approach for remote photoplethysmography and cardiac activity monitoring leveraging minimal data","authors":"Saran Zeb ,&nbsp;Xiaocong Lian ,&nbsp;Wajid Mumtaz ,&nbsp;Kegang Wang","doi":"10.1016/j.bspc.2026.109787","DOIUrl":"10.1016/j.bspc.2026.109787","url":null,"abstract":"<div><div>Precise heart rate measurement is crucial for assessing an individual’s health, offering valuable insights into their heart condition and cardiovascular activity. Remote photoplethysmography (rPPG) provides a contactless technique for measuring heart rate and monitoring cardiac activity without direct skin contact. This method is especially advantageous in scenarios where direct contact is not feasible or desirable, such as during pandemics to avoid infection risk. Despite significant progress, rPPG still faces challenges, including variations in illumination, motion artifacts, sensor noise, and skin tone variations, which complicate accurate waveform capture. This study proposes a deep neural network model comprising convolutional and LSTM layers to accurately detect the blood volume pulse (BVP) and monitor cardiac activity from RGB and NIR video frames. Notably, the model was trained on single-subject, single-channel data, effectively predicting the PPG waveform and estimating heart rate. Furthermore, the model was trained and tested on videos from four different devices—a webcam, smartphone, RealSense color camera, and NIR camera, ensuring robustness across various sensor types. The model was evaluated on four publicly available datasets—VIPL-HR, MR-NIRP, UBFC-rPPG, and MPSC-rPPG—achieving a mean absolute error (MAE) of 2.64 beats per minute for the same subject and 4.84 beats per minute for different subjects. Achieving robust and accurate heart rate estimation from a single subject across various sensors and challenging scenarios underscores the potential of this contactless model for cardiovascular assessment.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109787"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical modelling of atherogenesis: temperamental endothelial permeability","authors":"Shankar Narayan S ,&nbsp;Aishwarya R ,&nbsp;Nidhi S Vaishnaw","doi":"10.1016/j.bspc.2026.109718","DOIUrl":"10.1016/j.bspc.2026.109718","url":null,"abstract":"<div><div>A significant contributor to the development of atherosclerosis is endothelial dysfunction, which is typified by elevated permeability. In order to understand the intricate interactions among low-density lipoprotein (LDL), cytokines (A), inflammatory immune cells (M), endothelial permeability (E), and vascular remodeling (R), we construct an evolving mathematical model in the present research. Using PID control theory, we introduce a novel approach to modulate endothelial permeability, demonstrating how proportional <span><math><mrow><mo>(</mo><msub><mi>k</mi><mi>p</mi></msub><mo>=</mo><mn>0.01</mn><mo>)</mo></mrow></math></span>, integral <span><math><mrow><mo>(</mo><msub><mi>k</mi><mi>i</mi></msub><mo>=</mo><mn>0.001</mn><mo>)</mo></mrow></math></span>, and derivatives <span><math><mrow><mo>(</mo><msub><mi>k</mi><mi>d</mi></msub><mo>=</mo><mn>0.01</mn><mo>)</mo></mrow></math></span> control terms can stabilize the system and restore endothelial function. Our simulations reveal nonlinear relationships and critical thresholds, where a <span><math><mrow><mn>10</mn><mo>%</mo></mrow></math></span> increase in LDL leads to a <span><math><mrow><mn>25</mn><mo>%</mo></mrow></math></span> rise in endothelial permeability, highlighting the sensitivity of the endothelium to small changes in LDL levels. Heatmap and other plot analyses further elucidate the system’s dynamics, showing that low levels of LDL (below <span><math><mrow><mn>2</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>3</mn></mrow></msup><mi>g</mi><mo>.</mo><mi>c</mi><msup><mrow><mi>m</mi></mrow><mrow><mo>-</mo><mn>3</mn></mrow></msup></mrow></math></span>) and cytokines (below <span><math><mrow><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>7</mn></mrow></msup><mi>g</mi><mo>.</mo><mi>c</mi><msup><mrow><mi>m</mi></mrow><mrow><mo>-</mo><mn>3</mn></mrow></msup></mrow></math></span>) are sufficient to induce significant endothelial dysfunction. At higher concentrations, permeability stabilizes near <span><math><mrow><mi>E</mi><mo>≈</mo><mn>12</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>3</mn></mrow></msup><mi>c</mi><msup><mrow><mi>m</mi></mrow><mn>3</mn></msup><mo>/</mo><mrow><mo>(</mo><mi>g</mi><mo>.</mo><mi>d</mi><mi>a</mi><mi>y</mi><mo>)</mo></mrow></mrow></math></span>. These findings underscore the importance of early intervention and multi-targeted therapies to mitigate endothelial damage and slow atherosclerosis progression. This study advances our understanding of the molecular mechanisms driving endothelial permeability and provides a computational framework for designing personalised therapeutic strategies.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109718"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DAAP-NET: Automatic identification and quantitative analysis of gastric wall structure for cancer screening using gastric ultrasound images","authors":"Mustafain Rehman ,&nbsp;Zhijun Liu ,&nbsp;Miao Fan ,&nbsp;Ahsan Humayun ,&nbsp;Mingze Ding ,&nbsp;Bin Liu","doi":"10.1016/j.bspc.2026.109648","DOIUrl":"10.1016/j.bspc.2026.109648","url":null,"abstract":"<div><h3>Objectives</h3><div>Gastric cancer remains a significant public health challenge worldwide, ranked fifth in incidence and fourth in mortality among all malignant tumors. Early gastric cancer (EGC) detection is critical to improving survival rates.</div></div><div><h3>Methods</h3><div>We propose a new segmentation model, Dual Attention Atrous Pixel Network (DAAP-Net), designed to predict the gastric wall and stomach cavity in gastric ultrasound images. Post-segmentation, the predicted gastric wall mask extracts a region of interest (ROI) focused on the five-layer wall structure. The ROI undergoes a spatially diffused iterative enhancement (SDIE) technique to suppress intra-layer noise while preserving inter-layer transitions. We apply edge detection to the SDIE-refined ROI and compute layer thickness as pixel distances between successive edges, and normalize them into the proportion vector <span><math><mrow><mtext>x</mtext></mrow></math></span>. A scalar deviation <span><math><mrow><mtext>d</mtext></mrow></math></span> from the normal baseline quantifies abnormality.</div></div><div><h3>Results</h3><div>DAAP-Net outperforms state-of-the-art segmentation methods, achieving Intersection over Union scores of 0.7720 ± 0.0618 for normal gastric wall, 0.9007 ± 0.0495 for normal stomach cavity, 0.7607 ± 0.0780 for cancer gastric wall, and 0.8843 ± 0.0561 for cancer stomach cavity. Quantitative analysis shows gastric wall layer parameters differ markedly; the edge-derived deviation metric <span><math><mrow><mtext>d</mtext></mrow></math></span> separates cohorts, with normal mean 0.128 and cancer mean 0.508.</div></div><div><h3>Conclusions</h3><div>Our research highlights structural differences between normal and cancerous gastric walls, providing a reliable and non-invasive method for EGC detection. Current limitations include manual ROI selection and occasional errors in low-contrast regions. Future work includes automated ROI selection, adding a benign-labeled cohort, a multi-center dataset, and improving model accuracy for real-time clinical applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109648"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT based air quality monitoring and asthma alerts driven by non-crossing quantile regression neural networks","authors":"Abhijit Das ,&nbsp;B.M. Chandrakala ,&nbsp;N Shobha ,&nbsp;J. Reshma ,&nbsp;Vikranth Bhoothpur ,&nbsp;Rakesh Kumar Godi","doi":"10.1016/j.bspc.2026.109565","DOIUrl":"10.1016/j.bspc.2026.109565","url":null,"abstract":"<div><div>Asthma is a chronic respiratory disease that remains difficult to manage due to variable symptoms and diverse environmental triggers. Conventional monitoring approaches often rely on costly equipment and subjective self-reports, limiting timely interventions. Moreover, existing deep learning models suffer from issues like limited data quality, poor handling of outliers and lack of accurate risk assessment. To overcome these complications, IoT Based Air Quality Monitoring and Asthma Alerts Driven by Non-Crossing Quantile Regression Neural Networks (AM-IoT-NCQRNN) is proposed. Initially, the data is collected from Air Quality and Health Impact Dataset. Then the input data is preprocessed under Robust Maximum Correntropy Kalman Filter (RMCKF) to handle missing elements, noise and outliers. RMCKF is for its correntropy-based similarity, offering superior outlier suppression compared to median filters, low-rank imputation and standard Kalman filtering. Afterwards, the preprocessed data is given to the Non-Crossing Quantile Regression Neural Network (NCQRNN) which predicts and classifies health impact scores of asthma as very high, high, moderate, very low and low. NCQRNN applies a non-crossing quantile constraint, ensuring stable and interpretable risk estimation compared to Regression Neural Networks (RNNs) that yield inconsistent boundaries under fluctuating inputs. The proposed approach is implemented as a smartphone application, with real-time data collected through an IoT-based system using a Raspberry Pi and estimated using metrics such as accuracy, precision, recall, f1-score, specificity, ROC and computational time. Finally, the performance of proposed AM-IoT-NCQRNN method attains 19.76%, 24.00% and 19.07% higher accuracy and 29.56%, 24.22% and 28.57% higher precision when compared with existing methods.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109565"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cortical network dynamics and neural decoding of fine motor complexity via fNIRS and attention-based deep learning","authors":"Xing Ji ,&nbsp;Zhong Yin ,&nbsp;Yifei Bi ,&nbsp;Kaiwei Yu ,&nbsp;Yize Li ,&nbsp;Jiafa Chen ,&nbsp;Dawei Zhang","doi":"10.1016/j.bspc.2026.109758","DOIUrl":"10.1016/j.bspc.2026.109758","url":null,"abstract":"<div><div>Fine motor decline serves as a critical early biomarker for neurodegenerative diseases like Parkinson’s disease, making its accurate assessment essential for early detection and intervention. While functional near-infrared spectroscopy (fNIRS) offers a portable, non-invasive neuroimaging solution, the precise cortical dynamics underlying varying levels of motor complexity remain underexplored. This study aims to investigate how fine motor task complexity modulates cortical activation and functional network topology. A secondary objective is to develop and validate a high-performance deep learning model to classify motor complexity levels from fNIRS signals. fNIRS data were recorded from healthy participants performing five fine-motor tasks of increasing complexity, and activation analyses were combined with graph-theoretical metrics to characterize neurophysiological responses. To classify the complexity of fine motor tasks from fNIRS signals, this study developed a bidirectional long short-term memory (Bi-LSTM) model. Performance evaluation used leave-one-out cross-validation, supplemented by multi-seed training to improve robustness. The model achieved an average classification accuracy of 90.67% ± 7.07% (95% CI: ± 2.68%) and an AUC of 0.9720 ± 0.0431, outperforming traditional support vector machine (by 21.3%) and Bi-LSTM (by 10.97%). These results demonstrate the model’s strong generalization across subjects and its ability to capture temporal-spatial patterns of cortical activation associated with increasing task complexity, providing a promising foundation for fine motor decoding and adaptive neurorehabilitation.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109758"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D CNN-based method for automatic reorientation of 11C-acetate cardiac PET images using anchor point detection","authors":"Shuai Liu ,&nbsp;Tan Gong ,&nbsp;Ximin Shi ,&nbsp;Xue Lin ,&nbsp;Ligang Fang ,&nbsp;Xiaoying Tang ,&nbsp;Fei Shang ,&nbsp;Li Huo","doi":"10.1016/j.bspc.2026.109814","DOIUrl":"10.1016/j.bspc.2026.109814","url":null,"abstract":"<div><h3>Background</h3><div>Interpreting and diagnosing cardiac PET images in the transaxial plane could complicate image assessment and hinder the detection of perfusion defects. Therefore, reorienting cardiac PET images from the transaxial plane to the short-axis plane is essential.</div></div><div><h3>Purpose</h3><div>A convolutional neural network (CNN)-based method for anchor point detection was proposed to enable the automatic reorientation of ¹¹C-acetate cardiac PET images.</div></div><div><h3>Methods</h3><div>A total of 57 subjects who underwent ¹¹C-acetate PET/CT imaging were enrolled in this study. Forty subjects were assigned to the training set, and 17 subjects to the testing set. Three anchor points (the apex of the left ventricle, the center of the left ventricle base and the center of the right ventricle) were manually annotated and used as the gold standard. A 3D CNN incorporating residual modules and fully connected layers was developed to predict the coordinates of three anchor points. A composite loss function was designed to guide the model training.</div></div><div><h3>Results</h3><div>The predicted coordinates demonstrated a significant correlation with the gold standard (ICCs &gt; 0.75, p &lt; 0.05). Across 17 segments, the average normalized root mean square error (NRMSE) was below 0.082, and the average relative difference was less than 8.69%. No significant differences in pharmacokinetic parameters were observed between manual annotation and the proposed method (all p &gt; 0.05). An NRMSE of 0.053 was achieved on the simulated pseudo image.</div></div><div><h3>Conclusions</h3><div>The 3D CNN-based method for anchor point detection demonstrated performance comparable to the manual approach, providing a novel and effective solution for the reorientation of ¹¹C-acetate cardiac PET image.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109814"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of eleven federated learning strategies for multi-contrast image-to-image MRI data synthesis from heterogeneous sources","authors":"Jan Fiszer ,&nbsp;Dominika Ciupek ,&nbsp;Maciej Malawski ,&nbsp;Tomasz Pieciak","doi":"10.1016/j.bspc.2026.109649","DOIUrl":"10.1016/j.bspc.2026.109649","url":null,"abstract":"<div><div>Deep learning (DL)-based image synthesis has recently gained enormous interest in medical imaging, allowing for generating multi-contrast data and therefore, the recovery of missing samples from interrupted or artefact-distorted acquisitions. However, the accuracy of DL models heavily relies on the representativeness of the training datasets naturally characterized by their distributions, experimental setups or preprocessing schemes. These complicate generalizing DL models across multi-site heterogeneous datasets while maintaining the confidentiality of the data. One of the possible solutions is to employ federated learning (FL), which enables the collaborative training of a DL model in a decentralized manner, demanding the involved sites to share only the characteristics of the models without transferring their sensitive medical data. The paper presents a DL-based magnetic resonance (MR) data translation in a FL way. We introduce a new aggregation strategy called FedBAdam that couples two methods with complementary strengths by incorporating momentum in the aggregation scheme and skipping the batch normalization layers. The work comprehensively validates 11 FL-based strategies for an image-to-image multi-contrast MR translation, considering healthy and tumorous brain scans from five different institutions. Our study has revealed that the FedBAdam achieves superior results in terms of mean squared error and structural similarity index compared with standard FL-based aggregation techniques, such as FedAvg or FedProx, and is on par with or superior to personalised methods, while exhibiting more stable convergence in a multi-site, multi-vendor, heterogeneous environment. The FedBAdam has prevented the overfitting of the model and gradually reached the optimal model parameters, exhibiting no oscillations.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109649"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MD-SIRNet: Multi-domain representations for EEG-based speech imagery recognition with deep learning","authors":"Liang Dong ,&nbsp;Hengyi Shao ,&nbsp;Zhejun Zhang ,&nbsp;Yingqi Zhu ,&nbsp;Shaoting Guo ,&nbsp;Lin Zhang ,&nbsp;Lei Li","doi":"10.1016/j.bspc.2026.109817","DOIUrl":"10.1016/j.bspc.2026.109817","url":null,"abstract":"<div><div>Speech imagery (SI) recognition from Electroencephalography (EEG), enhances the foundation of the brain-computer interface (BCI). Although some existing research has been proposed to solve the high variability and low signal-to-noise ratio of multi-channel EEG signals, the spatial–temporal-frequency information is still underutilized to improve the performance of SI recognition. We propose MD-SIRNet to obtain multi-domain features efficiently and precisely. MD-SIRNet decomposes spatial multi-channel EEG data into four sets of intrinsic mode functions (IMFs) using Multivariate Variational Mode Decomposition (MVMD). To emphasize the main features, the IMFs are summed and then transformed into time–frequency representation (TFR) images after extracting high-precision time–frequency features using the Synchrosqueezed Wavelet Transform (SSWT). TFR images are fed into the Tuned-CNN model. MD-SIRNet is validated on two publicly available EEG datasets, compared with five methods by accuracy. The results of MD-SIRNet achieve an accuracy improvement of 2.23%, 0.45%, 1.59%, 4.45%, and 5.56% for long words, long-short words, short words, vowels, and command words. The code and model are available at <span><span>https://github.com/buptantEEG/MD-SIRNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"119 ","pages":"Article 109817"},"PeriodicalIF":4.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}