Journal of Neuroscience Methods最新文献

{"title":"Eye-movement artifact correction in infant EEG: A systematic comparison between ICA and Artifact Blocking","authors":"Diksha Srishyla ,&nbsp;Sara Jane Webb ,&nbsp;Mayada Elsabbagh ,&nbsp;Christian O’Reilly ,&nbsp;BASIS Team","doi":"10.1016/j.jneumeth.2025.110405","DOIUrl":"10.1016/j.jneumeth.2025.110405","url":null,"abstract":"<div><h3>Background:</h3><div>Independent Component Analysis (ICA) is a well-established approach to clean EEG and remove the impact of signals of non-neural origin, such as those from muscular activity and eye movements. However, evidence suggests that ICA removes artifacts less effectively in infants than in adults. This study systematically compares ICA and Artifact Blocking (AB), an alternative approach proposed to improve eye-movement artifact correction in infant EEG.</div></div><div><h3>Methods:</h3><div>We analyzed EEG collected from 50 infants between 6 and 18 months of age as part of the International Infant EEG Data Integration Platform (EEG-IP), a longitudinal multi-study dataset. EEG was recorded while infants sat on their caregivers’ laps and watched videos. We used ICA and AB to correct for eye-movement artifacts in the EEG and calculated the proportion of effectively corrected segments, signal-to-noise ratio (SNR), power-spectral density (PSD), and multiscale entropy (MSE) in manually selected EEG segments with and without eye-movement artifacts.</div></div><div><h3>Results:</h3><div>On the one hand, the proportion of effectively corrected segments indicated that ICA corrected eye-movement artifacts (sensitivity) better than AB. SNR and PSD indicated that both AB and ICA correct eye-movement artifacts with equal sensitivity. MSE gave mixed results. On the other hand, AB caused less distortion to the clean segments (specificity) for SNR, PSD, and MSE.</div></div><div><h3>Conclusion:</h3><div>Our results suggest that ICA is more sensitive (i.e., it better removes artifacts) but less specific (it distorts clean signals) than AB for correcting eye-movement artifacts in infant EEG.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110405"},"PeriodicalIF":2.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain tumor segmentation with deep learning: Current approaches and future perspectives","authors":"Akash Verma,&nbsp;Arun Kumar Yadav","doi":"10.1016/j.jneumeth.2025.110424","DOIUrl":"10.1016/j.jneumeth.2025.110424","url":null,"abstract":"<div><h3>Background:</h3><div>Accurate brain tumor segmentation from MRI images is critical in the medical industry, directly impacts the efficacy of diagnostic and treatment plans. Accurate segmentation of tumor region can be challenging, especially when noise and abnormalities are present.</div></div><div><h3>Method:</h3><div>This research provides a systematic review of automatic brain tumor segmentation techniques, with a specific focus on the design of network architectures. The review categorizes existing methods into unsupervised and supervised learning techniques, as well as machine learning and deep learning approaches within supervised techniques. Deep learning techniques are thoroughly reviewed, with a particular focus on CNN-based, U-Net-based, transfer learning-based, transformer-based, and hybrid transformer-based methods.</div></div><div><h3>Scope and Coverage:</h3><div>This survey encompasses a broad spectrum of automatic segmentation methodologies, from traditional machine learning approaches to advanced deep learning frameworks. It provides an in-depth comparison of performance metrics, model efficiency, and robustness across multiple datasets, particularly the BraTS dataset. The study further examines multi-modal MRI imaging and its influence on segmentation accuracy, addressing domain adaptation, class imbalance, and generalization challenges.</div></div><div><h3>Comparison with existing methods:</h3><div>The analysis highlights the current challenges in Computer-aided Diagnostic (CAD) systems, examining how different models and imaging sequences impact performance. Recent advancements in deep learning, especially the widespread use of U-Net architectures, have significantly enhanced medical image segmentation. This review critically evaluates these developments, focusing the iterative improvements in U-Net models that have driven progress in brain tumor segmentation. Furthermore, it explores various techniques for improving U-Net performance for medical applications, focussing on its potential for improving diagnostic and treatment planning procedures.</div></div><div><h3>Conclusion:</h3><div>The efficiency of these automated segmentation approaches is rigorously evaluated using the BraTS dataset, a benchmark dataset, part of the annual Multimodal Brain Tumor Segmentation Challenge (MICCAI). This evaluation provides insights into the current state-of-the-art and identifies key areas for future research and development.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110424"},"PeriodicalIF":2.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-level visual processing in the lateral geniculate nucleus revealed using goal-driven deep learning","authors":"Mai Gamal ,&nbsp;Seif Eldawlatly","doi":"10.1016/j.jneumeth.2025.110429","DOIUrl":"10.1016/j.jneumeth.2025.110429","url":null,"abstract":"<div><h3>Background</h3><div>The Lateral Geniculate Nucleus (LGN) is an essential contributor to high-level visual processing despite being an early subcortical area in the visual system. Current LGN computational models focus on its basic properties, with less emphasis on its role in high-level vision.</div></div><div><h3>New method</h3><div>We propose a high-level approach for encoding mouse LGN neural responses to natural scenes. This approach employs two deep neural networks (DNNs); namely VGG16 and ResNet50, as goal-driven models. We use these models as tools to better understand visual features encoded in the LGN.</div></div><div><h3>Results</h3><div>Early layers of the DNNs represent the best LGN models. We also demonstrate that numerosity, as a high-level visual feature, is encoded, along with other visual features, in LGN neural activity. Results demonstrate that intermediate layers are better in representing numerosity compared to early layers. Early layers are better at predicting simple visual features, while intermediate layers are better at predicting more complex features. Finally, we show that an ensemble model of an early and an intermediate layer achieves high neural prediction accuracy and numerosity representation.</div></div><div><h3>Comparison with existing method(s)</h3><div>Our approach emphasizes the role of analyzing the inner workings of DNNs to demonstrate the representation of a high-level feature such as numerosity in the LGN, as opposed to the common belief about the simplicity of the LGN.</div></div><div><h3>Conclusions</h3><div>We demonstrate that goal-driven DNNs can be used as high-level vision models of the LGN for neural prediction and as an exploration tool to better understand the role of the LGN.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110429"},"PeriodicalIF":2.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuromuscular information transmission patterns for human motor identification on non-invasive tFUS brain signal","authors":"ShuSheng Zhu","doi":"10.1016/j.jneumeth.2025.110431","DOIUrl":"10.1016/j.jneumeth.2025.110431","url":null,"abstract":"<div><h3>Research background</h3><div>This study investigates neuromuscular information transmission patterns facilitated by non-invasive transcranial focused ultrasound (tFUS), a novel neuromodulation technique. The research explores how neuromodulation via tFUS influences motor unit action potentials (MUAPs) and their coherence with synchronized EEG signals during varying motor tasks.</div></div><div><h3>Methods and methodology</h3><div>EEG and surface electromyography (sEMG) signals were recorded from nine healthy subjects performing motor tasks at 15 % and 30 % maximum voluntary contraction (MVC). Morphological decomposition and template reconstruction were applied to decompose sEMG signals into their fundamental components. MUAP features—amplitude, quantity, and firing rate—were extracted and analyzed. The study employed Transfer Entropy to measure the coherence between MUAP features and EEG signals, assessing the impact of tFUS on cortical-muscle interactions.</div></div><div><h3>Result analysis</h3><div>The analysis revealed that MUAP features, particularly amplitude, were significantly enhanced at higher grip strength levels (30 % MVC). The MUAP amplitude emerged as the most responsive feature, reflecting cortical activity peaks and troughs with high sensitivity.</div></div><div><h3>Comparison with previous studies</h3><div>Unlike prior studies focusing on overall muscle electrical signals, this research used sEMG decomposition to obtain granular MUAP features, offering richer insights into neuromuscular dynamics. The findings align with the \"size principle\" of motor unit recruitment, confirming that larger MUAPs are recruited at higher force levels.</div></div><div><h3>Conclusion</h3><div>Moreover, the use of tFUS, an emerging NIBS modality, extends previous research by demonstrating its efficacy in modulating brain-muscle interactions and enhancing the coupling between cortical and muscular systems.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110431"},"PeriodicalIF":2.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A refined Golgi-Cox method for the staining of embryonic neurons in the mouse brain","authors":"Benjamin Mitchell ,&nbsp;Cooper Atterton ,&nbsp;Darryl Whitehead ,&nbsp;Stefan Thor ,&nbsp;Michael Piper","doi":"10.1016/j.jneumeth.2025.110432","DOIUrl":"10.1016/j.jneumeth.2025.110432","url":null,"abstract":"<div><div>The Golgi-Cox stain remains a valuable technique used to investigate the morphology of individual neurons. Despite this, Golgi-Cox staining protocols are predominantly designed to impregnate adult neurons. Protocols optimised for the staining of immature embryonic mouse neurons have been previously developed but have limitations, including being time-consuming and being reliant on the use of expensive commercial kits. Here, we present a simple and inexpensive method for Golgi-Cox staining of embryonic neurons in the mouse brain. We identified that a 60 minute, 4 % paraformaldehyde (PFA) brain fixation step, followed by a wash with distilled water prior to immersion in Golgi-Cox solution was critical to the success of the stain. By altering the duration of the wash step, the visualisation of different populations across the neuraxis of neurons could be emphasised. Shorter washes enabled cortical neurons to be readily distinguished, whereas extending the wash steps was needed to enable subcortical neurons to be delineated.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110432"},"PeriodicalIF":2.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic resonance spectroscopy and the menstrual cycle: A multi-centre assessment of menstrual cycle effects on GABA & GSH","authors":"Yulu Song ,&nbsp;James J. Prisciandaro ,&nbsp;Dace Apšvalka ,&nbsp;Mae Bernard ,&nbsp;Adam Berrington ,&nbsp;Miguel Castelo-Branco ,&nbsp;Mark K. Britton ,&nbsp;Marta M. Correia ,&nbsp;Koen Cuypers ,&nbsp;Aleksandra Domagalik ,&nbsp;Ulrike Dydak ,&nbsp;Niall W. Duncan ,&nbsp;Gerard E. Dwyer ,&nbsp;Tao Gong ,&nbsp;Ian Greenhouse ,&nbsp;Katarzyna Hat ,&nbsp;Melina Hehl ,&nbsp;Shiori Honda ,&nbsp;Chris Horton ,&nbsp;Steve C.N. Hui ,&nbsp;Katherine Dyke","doi":"10.1016/j.jneumeth.2025.110430","DOIUrl":"10.1016/j.jneumeth.2025.110430","url":null,"abstract":"<div><h3>Background</h3><div>Gamma-aminobutyric acid (GABA) and glutathione (GSH) play a significant role in the functioning of a healthy brain and can both be quantified using magnetic resonance spectroscopy (MRS). Several small-scale studies have suggested MRS measured GABA may fluctuate with the menstrual cycle, but the effects on GSH are unknown. Utilising recent developments in MRS acquisition, this multi-lab study explores this issue across 4 distinctive brain regions.</div></div><div><h3>New methods</h3><div>Data were analysed from 12 independent sites from which a total of 30 women were scanned during three phases of their menstrual cycle corresponding to early follicular, ovulation and mid luteal phases. HERMES and HERCULES sequences were used to measure GABA and GSH in voxels located in the left motor cortex, left posterior insular, medial parietal and medial frontal. Linear mixed models were used to assess the variability contributed by site, participant and menstrual cycle phase.</div></div><div><h3>Results</h3><div>Similar variance was attributed to site and menstrual cycle phase for both GABA and GSH data. No systematic changes in GABA or GSH were revealed for any voxel as a consequence of menstrual cycle phase.</div></div><div><h3>Comparison with existing methods</h3><div>Despite our larger sample size and inclusion of more brain regions we fail to replicate previous findings of GABA change as a consequence of menstrual cycle phase. We also show for the first time that MRS measures of GSH so not significantly alter with cycle.</div></div><div><h3>Conclusions</h3><div>Our findings suggest that the menstrual cycle has minimal impact on MRS measures of GABA and GSH. The presence of a menstrual cycle should not be used as justification for exclusion of women in MRS studies.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110430"},"PeriodicalIF":2.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “An MRI protocol for anatomical and functional evaluation of the California sea lion brain” [J. Neurosci. Methods 353 (2021) 109097]","authors":"Peter F. Cook ,&nbsp;Vanessa A. Hoard ,&nbsp;Sudipto Dolui ,&nbsp;Blaise deB Frederick ,&nbsp;Richard Redfern ,&nbsp;Sophie E. Dennison ,&nbsp;Barbie Halaska ,&nbsp;Josh Bloom ,&nbsp;Kris T. Kruse-Elliott ,&nbsp;Emily R. Whitmer ,&nbsp;Emily J. Trumbull ,&nbsp;Gregory S. Berns ,&nbsp;John A. Detre ,&nbsp;Mark D'Esposito ,&nbsp;Frances M.D. Gulland ,&nbsp;Colleen Reichmuth ,&nbsp;Shawn P. Johnson ,&nbsp;Cara L. Field ,&nbsp;Ben A. Inglis","doi":"10.1016/j.jneumeth.2025.110423","DOIUrl":"10.1016/j.jneumeth.2025.110423","url":null,"abstract":"","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110423"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential mechanism and efficacy evaluation of transcranial focused ultrasound therapy for Alzheimer's disease","authors":"Wanqi Qin,&nbsp;Jiayi He,&nbsp;Yi Zhou","doi":"10.1016/j.jneumeth.2025.110428","DOIUrl":"10.1016/j.jneumeth.2025.110428","url":null,"abstract":"<div><h3>Background</h3><div>Transcranial focused ultrasound (TFU) is emerging as a promising non-invasive therapy capable of blood-brain barrier (BBB) opening. TFU potentially allows the transfer of therapeutic agents to targeted brain areas for patients affected with Alzheimer's disease (AD).</div></div><div><h3>New method</h3><div>The efficacy and mechanism of TFU in modulating BBB permeability in key brain regions, including the hippocampus and frontal lobe, are investigated in this research. A total of 20 participants aged 60–85 years were involved with AD. The treatment protocol involved three TFU sessions, spaced three weeks apart. The research encompasses pre-assessment and post-assessment of treatment with follow-up ranging from 5 to 12 months.</div></div><div><h3>Results</h3><div>Statistical analysis involved paired t-tests to compare pre- and post-treatment cognitive scores, and ANOVA to predict significant differences in amyloid reduction across different brain regions, with the high decrease observed in the hippocampus. Multivariate Analysis (MANOVA) to explore the combined effect of cognitive and amyloid data. Linear Regression Analysis to predict cognitive improvement from amyloid plaque reduction. Longitudinal analysis for time-to-event analysis assessing the durability of effects over time.</div></div><div><h3>Comparison with existing methods</h3><div>Florbetaben Positron Emission Tomography (PET) scans demonstrated a reduction in β-amyloid plaque burden, with a 15 % average decrease in the treated brain regions. No adverse effects on disease progression were observed up to 1 year after treatment.</div></div><div><h3>Conclusion</h3><div>This analysis presents the largest cohort of AD patients treated with TFU, with the longest follow-up to date. The treatment demonstrated safety and feasibility, with reversible BBB opening in multiple brain regions.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110428"},"PeriodicalIF":2.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in conduction velocities of nerve fibers excited by infrared and electrical stimulation","authors":"Louis Vande Perre ,&nbsp;Javier Chávez Cerda ,&nbsp;Soizic Gochard ,&nbsp;Maxime Verstraeten ,&nbsp;Romain Raffoul ,&nbsp;Catherine Leonard ,&nbsp;Jean Delbeke ,&nbsp;Riëm El Tahry ,&nbsp;Simon-Pierre Gorza ,&nbsp;Antoine Nonclercq","doi":"10.1016/j.jneumeth.2025.110427","DOIUrl":"10.1016/j.jneumeth.2025.110427","url":null,"abstract":"<div><h3>Background</h3><div>Infrared neural stimulation (INS) uses short optical pulses to activate nerves. While electrical stimulation (ES) activates large-diameter fibers first, light may preferentially activate small-diameter fibers first, which could be valuable for many clinical applications.</div></div><div><h3>New method</h3><div>This study used a compact diode laser of 1470 nm to perform INS. Conduction velocity (CV) measurements were performed to assess differences in fiber type activation between INS and ES in the rat sciatic nerve and the goat vagus nerve. The rat sciatic nerve was chosen as a standard model because of its well-characterized physiology and extensive use in studies of INS mechanisms. The goat vagus nerve was chosen because of its expected high proportion of small-diameter fibers and its larger size, which allows sufficient separation between recording units to optimize CNAP measurements.</div></div><div><h3>Results</h3><div>The results showed that in the rat sciatic nerve, ES-excited fibers had significantly higher CVs (9.81 ± 3.18 m/s) than INS-excited fibers (8.10 ± 2.82 m/s). In the goat vagus nerve, ES produced a mean CV of 6.47 ± 1.25 m/s, but INS did not produce clearly distinguishable compound nerve action potential, highlighting the challenges of applying INS to larger nerves.</div></div><div><h3>Comparison to existing methods</h3><div>To the best of our knowledge, CV is, for the first time, measured to identify the type of nerve fiber excited by INS.</div></div><div><h3>Conclusion</h3><div>These results suggest that INS may preferentially activate smaller diameter fibers, providing insight for potential neuromodulation applications.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110427"},"PeriodicalIF":2.7,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-pathway EEG model with channel attention for virtual reality motion sickness detection","authors":"Chengcheng Hua ,&nbsp;Yuechi Chen ,&nbsp;Jianlong Tao ,&nbsp;Zhian Dai ,&nbsp;Wenqing Yang ,&nbsp;Dapeng Chen ,&nbsp;Jia Liu ,&nbsp;Rongrong Fu","doi":"10.1016/j.jneumeth.2025.110425","DOIUrl":"10.1016/j.jneumeth.2025.110425","url":null,"abstract":"<div><h3>Background</h3><div>Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) and limiting the development of the VR industry. Accurate detection of Virtual Reality Motion Sickness (VRMS) is a prerequisite for solving the problem.</div></div><div><h3>New method</h3><div>In this paper, a dual-pathway model with channel attention for detecting VRMS is proposed. The proposed model has two pathways that both consist of CNN blocks and channel attention modules. The first pathway takes the EEG signal as inputs. The second pathway transforms the EEG signal into brain networks of six frequency bands using Phase Locking Value (PLV) or ρ index (RHO) methods and takes the adjacent matrixes as input. The features from the two pathways are connected and fed into the fully connected layer for classification. Finally, a VR flight simulation experiment is performed and the EEG of the resting state before and after the virtual flight task are collected to validate the model.</div></div><div><h3>Results</h3><div>The average accuracy, precision, recall, and F1 score of the proposed model are 99.12 %, 99.12 %, 99.11 %, and 99.12 %, respectively.</div></div><div><h3>Comparison with existing methods</h3><div>Eight models are introduced as the reference methods and four of them are fused as the hybrid models in this study. The results show that the proposed model is better than those state-of-art models.</div></div><div><h3>Conclusions</h3><div>The proposed model outperforms the state-of-the-art models and provides objective and direct guidance for overcoming VRMS and optimizing VR experience.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110425"},"PeriodicalIF":2.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}