Neuroscience informatics最新文献

{"title":"Deep learning model for patient emotion recognition using EEG-tNIRS data","authors":"Mohan Raparthi ,&nbsp;Nischay Reddy Mitta ,&nbsp;Vinay Kumar Dunka ,&nbsp;Sowmya Gudekota ,&nbsp;Sandeep Pushyamitra Pattyam ,&nbsp;Venkata Siva Prakash Nimmagadda","doi":"10.1016/j.neuri.2025.100219","DOIUrl":"10.1016/j.neuri.2025.100219","url":null,"abstract":"<div><div>This study presents a novel approach that integrates electroencephalogram (EEG) and functional near-infrared spectroscopy (tNIRS) data to enhance emotion classification accuracy. A Modality-Attentive Multi-Channel Graph Convolution Model (MAMP-GF) is introduced, leveraging GraphSAGE-based representation learning to capture inter-channel relationships. Multi-level feature extraction techniques, including Channel Features (CF), Statistical Features (SF), and Graph Features (GF), are employed to maximize the discriminative power of EEG-tNIRS signals. To enhance modality fusion, we propose and evaluate three fusion strategies: MA-GF, MP-GF, and MA-MP-GF, which integrate graph convolutional networks with a modality attention mechanism. The model is trained and validated using EEG and tNIRS data collected from 30 subjects exposed to emotionally stimulating video clips. Experimental results demonstrate that the proposed MA-MP-GF fusion model achieves 98.77% accuracy in subject-dependent experiments, significantly outperforming traditional single-modal and other multimodal fusion methods. In cross-subject validation, the model attains a 55.53% accuracy, highlighting its robustness despite inter-subject variability. The findings illustrate that the proposed graph convolution fusion approach, combined with modality attention, effectively enhances emotion recognition accuracy and stability. This research underscores the potential of EEG-tNIRS fusion in real-time, non-invasive emotion monitoring, paving the way for advanced applications in personalized healthcare and affective computing.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning in artificial intelligence and neurobiology","authors":"Tursun Alkam,&nbsp;Andrew H Van Benschoten,&nbsp;Ebrahim Tarshizi","doi":"10.1016/j.neuri.2025.100220","DOIUrl":"10.1016/j.neuri.2025.100220","url":null,"abstract":"<div><div>Reinforcement learning (RL), a computational framework rooted in behavioral psychology, enables agents to learn optimal actions through trial and error. It now powers intelligent systems across domains such as autonomous driving, robotics, and logistics, solving tasks once thought to require human cognition. As RL reshapes artificial intelligence (AI), it raises a critical question in neuroscience: does the brain learn through similar mechanisms? Growing evidence suggests it does.</div><div>To bridge this interdisciplinary gap, this review introduces core RL concepts to neuroscientists and clinicians with limited AI exposure. We outline the agent–environment interaction loop and describe key architectures including model-free, model-based, and meta-RL. We then examine how advances in deep RL have generated testable hypotheses about neural computation and behavior. In parallel, we discuss how neurobiological findings, especially the role of dopamine in encoding reward prediction errors, have inspired biologically grounded RL models. Empirical studies reveal neural correlates of RL algorithms in the basal ganglia, prefrontal cortex, and hippocampus, supporting their roles in planning, memory, and decision-making. We also highlight clinical applications, including how RL frameworks are used to model cognitive decline and psychiatric disorders, while acknowledging limitations in scaling RL to biological complexity.</div><div>Looking ahead, RL offers powerful tools for understanding brain function, guiding brain–machine interfaces, and personalizing psychiatric treatment. The convergence of RL and neuroscience offers a promising interdisciplinary lens for advancing our understanding of learning and decision-making in both artificial agents and the human brain.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100220"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-window EEG-based auditory attention decoding for neuroadaptive hearing support for smart healthcare","authors":"Ihtiram Raza Khan ,&nbsp;Sheng-Lung Peng ,&nbsp;Rupali Mahajan ,&nbsp;Rajesh Dey","doi":"10.1016/j.neuri.2025.100222","DOIUrl":"10.1016/j.neuri.2025.100222","url":null,"abstract":"<div><h3>Background</h3><div>Selective auditory attention the brain's ability to focus on a specific speaker in multi-talker environments is often compromised in individuals with auditory or neurological disorders. While Auditory Attention Decoding (AAD) using EEG has shown promise in detecting attentional focus, existing models primarily utilize temporal or spectral features, often neglecting the synergistic relationships across time, space, and frequency. This limitation significantly reduces decoding accuracy, particularly in short decision windows, which are crucial for real-time applications like neuro-steered hearing aids. This study is to enhance short-window AAD performance by fully leveraging multi-dimensional EEG characteristics.</div></div><div><h3>Methods</h3><div>To address this, we propose TSF-AADNet, a novel neural framework that integrates temporal–spatial and frequency–spatial features using dual-branch architectures and advanced attention-based fusion.</div></div><div><h3>Results</h3><div>Tested on KULeuven and DTU datasets, TSF-AADNet achieves 91.8% and 81.1% accuracy at 0.1-second windows—outperforming the state-of-the-art by up to 7.99%.</div></div><div><h3>Conclusions</h3><div>These results demonstrate the model's potential in enabling precise, real-time attention tracking for hearing impairment diagnostics and next-generation neuroadaptive auditory prosthetics.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal lightweight neural network for Alzheimer's disease diagnosis integrating neuroimaging and cognitive scores","authors":"Bhoomi Gupta ,&nbsp;Ganesh Kanna Jegannathan ,&nbsp;Mohammad Shabbir Alam ,&nbsp;Kottala Sri Yogi ,&nbsp;Janjhyam Venkata Naga Ramesh ,&nbsp;Vemula Jasmine Sowmya ,&nbsp;Isa Bayhan","doi":"10.1016/j.neuri.2025.100218","DOIUrl":"10.1016/j.neuri.2025.100218","url":null,"abstract":"<div><div>Conventional single-modal approaches for auxiliary diagnosis of Alzheimer's disease (AD) face several limitations, including insufficient availability of expertly annotated imaging datasets, unstable feature extraction, and high computational demands. To address these challenges, we propose Light-Mo-DAD, a lightweight multimodal diagnostic neural network designed to integrate MRI, PET imaging, and neuropsychological assessment scores for enhanced AD detection. In the neuroimaging feature extraction module, redundancy-reduced convolutional operations are employed to capture fine-grained local features, while a global filtering mechanism enables the extraction of holistic spatial patterns. Multimodal feature fusion is achieved through spatial image registration and summation, allowing for effective integration of structural and functional imaging modalities. The neurocognitive feature extraction module utilizes depthwise separable convolutions to process cognitive assessment data, which are then fused with multimodal imaging features. To further enhance the model's discriminative capacity, transfer learning techniques are applied. A multilayer perceptron (MLP) classifier is incorporated to capture complex feature interactions and improve diagnostic precision. Evaluation on the ADNI dataset demonstrates that Light-Mo-DAD achieves 98.0% accuracy, 98.5% sensitivity, and 97.5% specificity, highlighting its robustness in early AD detection. These results suggest that the proposed architecture not only enhances diagnostic accuracy but also offers strong potential for real-time, mobile deployment in clinical settings, supporting neurologists in efficient and reliable Alzheimer's diagnosis.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100218"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An automated measurement of head circumference using CT scans: An application in children with head abnormalities","authors":"Priscila Satomi Acamine ,&nbsp;Rafael Maffei Loureiro ,&nbsp;Lucas dos Anjos Longas ,&nbsp;Fabio Augusto Ribeiro Dalpra ,&nbsp;Luigi Villanova Machado de Barros Lago ,&nbsp;Larissa Vasconcellos de Moraes ,&nbsp;Paulo Cesar Filho Estevam ,&nbsp;Luiz Otávio Vittorelli ,&nbsp;Lucas Silva Kallás ,&nbsp;Ana Paula Antunes Pascalicchio Bertozzi ,&nbsp;Maria Isabel Barros Guinle ,&nbsp;Gilberto Szarf ,&nbsp;Saulo Duarte Passos ,&nbsp;Birajara Soares Machado ,&nbsp;Joselisa Péres Queiroz De Paiva","doi":"10.1016/j.neuri.2025.100217","DOIUrl":"10.1016/j.neuri.2025.100217","url":null,"abstract":"<div><div>Manual measurement of head circumference has been a widely adopted method of neurodevelopmental evaluation in both clinical and research settings. Here, we propose a method that uses axial slices of computerized tomography (CT) scans to detect the largest outer margin for measurement. Our method can both complement conventional tape measurements or be applied as a standalone tool, especially in the context of retrospective big data analysis. We applied our algorithm in a set of 74 head CT scans obtained from individual children (8,5 ± 14,1 months old). The method proved to be concordant <span><math><mo>(</mo><mrow><mi>ICC</mi></mrow><mo>[</mo><mn>2</mn><mo>,</mo><mi>k</mi><mo>]</mo><mo>=</mo><mn>0.99</mn><mo>)</mo></math></span>, consistent (<span><math><mrow><mi>ICC</mi></mrow><mo>[</mo><mn>3</mn><mo>,</mo><mi>k</mi><mo>]</mo></math></span> = 1), and showed a correlation of 0.988 compared to obtaining manual head circumferences by specialists. Our method is a reliable alternative to conventional manual measurements of head circumference. It can be readily applied in macrocephaly and microcephaly screening studies and in growth reference charts for syndromes related to head alterations.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the numbers: App-enabled stroke prediction system for high-risk individuals in imbalanced datasets","authors":"Abrar Faiaz Eram ,&nbsp;Aliva Sadnim Mahmud ,&nbsp;Marwan Mostafa Khadem ,&nbsp;Md Amimul Ihsan","doi":"10.1016/j.neuri.2025.100215","DOIUrl":"10.1016/j.neuri.2025.100215","url":null,"abstract":"<div><h3>Background:</h3><div>Brain stroke, characterized by interrupted blood flow to the brain, poses significant mortality risks and quality-of-life impacts. While machine learning approaches show promise in stroke prediction, current research often relies on synthetic data to address dataset imbalance, potentially compromising real-world model performance in clinical settings.</div></div><div><h3>Method:</h3><div>This research proposes an alternative approach focusing on recall as the primary evaluation metric for stroke prediction, specifically targeting the reduction of false negatives. In the context of stroke diagnosis, where missed detection can lead to severe consequences or fatality, recall is crucial for directly measuring the model's ability to identify actual stroke cases.</div></div><div><h3>Results:</h3><div>Three superior models were identified: Logistic Regression, an Ensemble using Soft Voting (combining Gaussian Naive Bayes and Logistic Regression), and customized Support Vector Machine. Exceptional stroke prediction was achieved with recall values of 92%, 92%, and 94%, respectively. Interpretability is enhanced through SHAP applied to the best one. While previous methods showed recall values between 5.6% and 40%, this approach outperformed these benchmarks (94%). Current research emphasizes accuracy metrics, relying on oversampling, being inappropriate for sensitive medical datasets. The pitfall is a slight increase in false positives, which is tolerable because the cost of misdiagnosing a stroke patient far outweighs the reverse scenario.</div></div><div><h3>Conclusions:</h3><div>The research demonstrates the effectiveness of focusing on recall as an evaluation metric for stroke prediction, minimizing false negative predictions. To facilitate practical implementation, a mobile application incorporating the best-performing model was included. A primary screening which can supplement doctors in stroke diagnosis and prediction was proposed.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory","authors":"Mohammed E. Seno ,&nbsp;Niladri Maiti ,&nbsp;Maulik Patel ,&nbsp;Mihirkumar M. Patel ,&nbsp;Kalpesh B. Chaudhary ,&nbsp;Ashish Pasaya ,&nbsp;Babacar Toure","doi":"10.1016/j.neuri.2025.100214","DOIUrl":"10.1016/j.neuri.2025.100214","url":null,"abstract":"<div><div>To address the limitations of traditional unimodal brain-computer interface BCI) technologies based on electroencephalography (EEG) such as low spatial resolution and high susceptibility to noise an increasing number of neuroscience-driven studies have begun to focus on BCI systems that fuse EEG signals with functional near-infrared spectroscopy (fNIRS) signals. However, integrating these two heterogeneous neurophysiological signals presents significant challenges. In this work, we propose an innovative end-to-end signal fusion method based on deep learning and evidence theory for motor imagery (MI) classification within the neuroscience domain. For EEG signals, spatiotemporal features are extracted using dual-scale temporal convolution and depthwise separable convolution, and a hybrid attention module is introduced to enhance the network's sensitivity to salient neural patterns. For fNIRS signals, spatial convolution across all channels is employed to explore activation differences among brain regions, and parallel temporal convolution combined with a gated recurrent unit (GRU) captures richer temporal dynamics of the hemodynamic response. At the decision fusion stage, decision outputs from both modalities are first quantified using Dirichlet distribution parameter estimation to model uncertainty, followed by a two-layer reasoning process using Dempster-Shafer Theory (DST) to fuse evidence from basic belief assignment (BBA) methods and both modalities. Experimental evaluation on the publicly available TU-Berlin-A dataset demonstrates the effectiveness of the proposed model, achieving an average accuracy of 83.26%, representing a 3.78% improvement over state-of-the-art methods. These results provide new insights and methodologies for neuroscience-inspired multimodal BCI systems integrating EEG and fNIRS signals.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting stroke with machine learning techniques in a sub-Saharan African population","authors":"Benjamin Segun Aribisala ,&nbsp;Deirdre Edward ,&nbsp;Godwin Ogbole ,&nbsp;Onoja M. Akpa ,&nbsp;Segun Ayilara ,&nbsp;Fred Sarfo ,&nbsp;Olusola Olabanjo ,&nbsp;Adekunle Fakunle ,&nbsp;Babafemi Oluropo Macaulay ,&nbsp;Joseph Yaria ,&nbsp;Joshua Akinyemi ,&nbsp;Albert Akpalu ,&nbsp;Kolawole Wahab ,&nbsp;Reginald Obiako ,&nbsp;Morenikeji Komolafe ,&nbsp;Lukman Owolabi ,&nbsp;Godwin Osaigbovo ,&nbsp;Akinkunmi Paul Okekunle ,&nbsp;Arti Singh ,&nbsp;Philip Ibinaye ,&nbsp;Mayowa Owolabi","doi":"10.1016/j.neuri.2025.100216","DOIUrl":"10.1016/j.neuri.2025.100216","url":null,"abstract":"<div><h3>Background</h3><div>Stroke is the second leading cause of death and the third leading cause of disability globally, including Africa, which bears its largest burden. Accurate models are needed in Africa to predict and prevent stroke occurrence. The aim of this study was to identify the best machine learning (ML) algorithm for stroke prediction.</div></div><div><h3>Methods</h3><div>We assessed medical data of 4,236 subjects comprising 2,118 stroke patients and 2,118 controls from the SIREN database. Sixteen established vascular risk factors were evaluated in this study. These are addition of salt to food at table during eating, cardiac disease, diabetes mellitus, dyslipidemia, education, family history of cardiovascular disease, hypertension, income, low green leafy vegetable consumption, obesity, physical inactivity, regular meat consumption, regular sugar consumption, smoking, stress and use of tobacco. From these, we also selected the 11 topmost risk factors using Population-Attributable Risk ranking. Eleven ML models were built and empirically investigated using the 16 and the 11 risk factors.</div></div><div><h3>Results</h3><div>Our results showed that the 16 features-based classification (maximum AUC of 82.32%) had a slightly better performance than the 11 feature-based (maximum AUC 81.17%) algorithm. The result also showed that Artificial Neural Network (ANN) had the best performance amongst eleven algorithms investigated with AUC of 82.32%, sensitivity of 71.23%, specificity of 80.00%.</div></div><div><h3>Conclusion</h3><div>Machine Learning algorithms predicted stroke occurrence employing major risk factors in Sub-Saharan Africa better than regression models. Machine Learning, especially Artificial Neural Network, is recommended to enhance Afrocentric stroke prediction models for stroke risk factor quantification and control in Africa.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100216"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring community pharmacist's psychological intentions to adopt generative artificial intelligence (GenAI) chatbots for patient information, education, and counseling","authors":"Hafidz Ihsan Hidayatullah ,&nbsp;Muhammad Taufiq Saifullah ,&nbsp;Muhammad Thesa Ghozali ,&nbsp;Ayesha Aziz","doi":"10.1016/j.neuri.2025.100213","DOIUrl":"10.1016/j.neuri.2025.100213","url":null,"abstract":"<div><div>Generative AI (GenAI) chatbots, driven by advanced machine learning algorithms, are emerging as transformative tools for enhancing patient education, information dissemination, and counseling (EIC) in healthcare. This study investigated the psychological determinants of community pharmacists' intentions to adopt GenAI chatbots using the Extended Technology Acceptance Model (ETAM). A cross-sectional survey of 240 licensed community pharmacists across several Indonesian provinces assessed key constructs, including self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEU), attitude toward technology (ATT), trust (TT), and behavioral intention (BI). Structural equation modeling revealed that SE significantly influenced PU (<span><math><mi>β</mi><mo>=</mo><mn>0.37</mn></math></span>) and PEU (<span><math><mi>β</mi><mo>=</mo><mn>0.57</mn></math></span>), indicating that confidence in using technology positively affects perceived utility and usability. PU further predicted ATT (<span><math><mi>β</mi><mo>=</mo><mn>0.39</mn></math></span>) and BI (<span><math><mi>β</mi><mo>=</mo><mn>0.236</mn></math></span>), emphasizing the motivational role of perceived benefits. Trust emerged as a crucial mediator, channeling favorable attitudes into actionable behavioral intentions (indirect <span><math><mi>β</mi><mo>=</mo><mn>0.148</mn></math></span>). The model demonstrated strong fit indices (<span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>263.09</mn></math></span>, RMSEA = 0.019, GFI = 0.915, CFI = 0.991), supporting the psychological framework. These findings highlight the importance of fostering trust, improving perceived usability, and enhancing self-efficacy through targeted training to promote GenAI chatbot adoption. Future research should explore longitudinal behavioral changes and contextual influences to support sustainable AI integration in pharmacy practice.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications","authors":"Suneet Gupta ,&nbsp;Praveen Gupta ,&nbsp;Bechoo Lal ,&nbsp;Aniruddha Deka ,&nbsp;Hirakjyoti Sarma ,&nbsp;Sheifali Gupta","doi":"10.1016/j.neuri.2025.100209","DOIUrl":"10.1016/j.neuri.2025.100209","url":null,"abstract":"<div><div>Accurate and non-invasive sleep staging is essential for evaluating sleep quality and diagnosing neurological and sleep disorders. Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. By enhancing discriminative features and suppressing irrelevant ones, the model generates a robust multimodal representation of sleep information. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network captures temporal dependencies in sleep stage transitions, improving classification accuracy. The effectiveness of MFDL is validated on the publicly available Sleep-EDF dataset, achieving 94.1% accuracy, 88.2% Kappa coefficient, and 81.9% MF1 score. Notably, the recall rates for the challenging N1 and REM sleep stages are significantly enhanced to 64.6% and 93.5%, respectively. These results highlight the potential of MFDL in enhancing tFUS-based neuromodulation by providing precise, data-driven sleep state monitoring, paving the way for advanced non-invasive brain stimulation technologies in next-gen clinical applications.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}