Journal of neural engineering最新文献

{"title":"Simulated prosthetic vision confirms checkerboard as an effective raster pattern for epiretinal implants.","authors":"Justin M Kasowski, Apurv Varshney, Roksana Sadeghi, Michael Beyeler","doi":"10.1088/1741-2552/adecc4","DOIUrl":"10.1088/1741-2552/adecc4","url":null,"abstract":"<p><p><i>Objective.</i>Spatial scheduling of electrode activation ('rastering') is essential for safely operating high-density retinal implants, yet its perceptual consequences remain poorly understood. This study systematically evaluates the impact of raster patterns, or spatial arrangements of sequential electrode activation, on performance and perceived difficulty in simulated prosthetic vision (SPV). By addressing this gap, we aimed to identify patterns that optimize functional vision in retinal implants.<i>Approach.</i>Sighted participants completed letter recognition and motion discrimination tasks under four raster patterns (horizontal, vertical, checkerboard, and random) using an immersive SPV system. The simulations emulated epiretinal implant perception and employed psychophysically validated models of electrode activation, phosphene appearance, nonlinear spatial summation, and temporal dynamics, ensuring realistic representation of prosthetic vision. Performance accuracy and self-reported difficulty were analyzed to assess the effects of raster patterning.<i>Main results.</i>The checkerboard pattern consistently outperformed other raster patterns, yielding significantly higher accuracy and lower difficulty ratings across both tasks. The horizontal and vertical patterns introduced biases aligned with apparent motion artifacts, while the checkerboard minimized such effects. Random patterns resulted in the lowest performance, underscoring the importance of structured activation. Notably, checkerboard matched performance in the 'No Raster' condition, despite conforming to groupwise safety constraints.<i>Significance.</i>This is the first quantitative, task-based evaluation of raster patterns in SPV. Checkerboard-style scheduling enhances perceptual clarity without increasing computational load, offering a low-overhead, clinically relevant strategy for improving usability in next-generation retinal prostheses.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preictal high-connectivity states in epilepsy: evidence of intracranial EEG, interplay with the seizure onset zone and network modeling.","authors":"Brian Nicolas Medina Leandro, Manel Vila-Vidal, Ana Tost, Mariam Khawaja, Mar Carreño, Pedro Roldán, Jordi Rumià, María Centeno, Estefanía Conde, Antonio Donaire, Adrià Tauste Campo","doi":"10.1088/1741-2552/adf097","DOIUrl":"https://doi.org/10.1088/1741-2552/adf097","url":null,"abstract":"<p><strong>Objective: </strong>Epilepsy affects around 50 million people worldwide, and reliable pre-seizure biomarkers could significantly improve neuromodulation therapies for drug-resistant patients. Recent research using stereo-electroencephalography (sEEG) has revealed transient changes in network dynamics preceding seizures. In particular, our previous work showed that these alterations are driven by recurrent, short-lasting (0.6 s) high-connectivity network configurations-termed High-Connectivity States (HCS). Here, we aim to replicate and further characterize HCS as a biomarker in a multicentric patient cohort, assess its robustness across recording modalities and montages, explore its relationship with interpretable physiological variables, and examine its network-level association with seizure-onset zone (SOZ) dynamics.&#xD;Approach. We analyzed long-term intracranial EEG (iEEG) recordings from 12 patients with sEEG and electrocorticography (ECoG). In two patients with extensive clinical information, we examined the interplay between HCS and SOZ dynamics. We also developed a low-dimensional stochastic network model to investigate mechanistic rationales of HCS emergence. Additionally, we compared HCS dynamics with gamma-band activity and heart rate, and tested robustness across different montage configurations.&#xD;Main Results. In most patients, HCS probability reliably increased hours before seizure onset. In the two deeply characterized patients, this increase was specifically linked to an increased network centrality within the SOZ. The network model revealed that changes in HCS probability stem primarily from topological reconfigurations rather than changes in mean connectivity, underscoring the importance of dynamic interactions between epileptogenic and non-epileptogenic regions.&#xD;Significance. These results support HCS probability as a promising biomarker for early seizure prediction and offer mechanistic insights into pre-seizure brain network dynamics.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651617","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":"Hearing restoration by a low-weight power-efficient multichannel optogenetic cochlear implant system.","authors":"Lukasz Jablonski, Tamas Harczos, Bettina Julia Wolf, Gerhard Hoch, Lakshay Khurana, Alexander Dieter, Lennart Roos, Roland Hessler, Suleman Ayub, Patrick Ruther, Tobias Moser","doi":"10.1088/1741-2552/adf00f","DOIUrl":"https://doi.org/10.1088/1741-2552/adf00f","url":null,"abstract":"<p><strong>Objective: </strong>In case of deafness, electrical cochlear implants (eCIs) bypass dysfunctional or lost hair cells by direct stimulation of the auditory nerve. However, spectral selectivity of eCI sound coding is low as the wide current spread from each electrode activates large sets of neurons along the tonotopic axis of the cochlea. As light can be better confined in space, optical cochlear implants (oCIs) combined with cochlear optogenetics promise to overcome this shortcoming of eCIs. This requires appropriate sound processing and control of multi-ple microscale emitters.</p><p><strong>Approach: </strong>Here, we describe the development, characterisation, and application of a preclinical miniaturised low-weight and wireless LED-based multi-channel oCI system for hearing restoration, and its comparison to its sister eCI system. We present exemplary implementation of these systems in behavioural studies on freely mov-ing rats.</p><p><strong>Main results: </strong>The system, which weights 15 g, is 20 mm in diameter and 20 mm in height, performed for up to 8 h in behavioural experiments on freely moving rats proving its utility for cueing auditory tasks in deaf animals.</p><p><strong>Significance: </strong>The head-worn oCI system enabled deafened rats to perform a locomotion task in response to acoustic stimulation proving the concept of multichannel optogenetic hearing restoration in rodents. This paves the way for implementation in other species and development of future clinical oCI systems for improved hearing restoration.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644476","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":"Personalizing brain stimulation: continual learning for sleep spindle detection.","authors":"Milo Sobral, Hugo R Jourde, S Ehsan M Bajestani, Emily B J Coffey, Giovanni Beltrame","doi":"10.1088/1741-2552/adebb1","DOIUrl":"10.1088/1741-2552/adebb1","url":null,"abstract":"<p><p><i>Objective.</i>Personalized stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications. Precise stimulation of sleep spindles-transient patterns of brain activity that occur during non rapid eye movement sleep that are involved in memory consolidation-presents an exciting frontier for studying memory functions; however, this endeavour is challenged by the spindles' fleeting nature, inter-individual variability, and the necessity of real-time detection.<i>Approach.</i>We tackle these challenges using a novel continual learning framework. Using a pre-trained model capable of both online classification of sleep stages and spindle detection, we implement an algorithm that refines spindle detection, tailoring it to the individual throughout one or more nights without manual intervention.<i>Main results.</i>Our methodology achieves accurate, subject-specific targeting of sleep spindles and enables advanced closed-loop stimulation studies. While fine-tuning alone offers minimal benefits for single nights, our approach combining weight averaging demonstrates significant improvement over multiple nights, effectively mitigating catastrophic forgetting.<i>Significance.</i>This work represents an important step towards signal-level personalization of brain stimulation that can be applied to different brain stimulation paradigms including closed-loop brain stimulation, and to different neural events. Applications in fundamental neuroscience may enhance the investigative potential of brain stimulation to understand cognitive processes such as the role of sleep spindles in memory consolidation, and may lead to novel therapeutic applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562374","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":"Motion artifact suppression method based on adaptive time-varying homogeneous field correction for OPM-MEG.","authors":"Chunhui Wang, Fuzhi Cao, Wen Li, Wenli Wang, Yong Li, Nan An, Min Xiang, Xiaolin Ning","doi":"10.1088/1741-2552/adec1d","DOIUrl":"10.1088/1741-2552/adec1d","url":null,"abstract":"<p><p><i>Objective.</i>Optically pumped magnetometer-based magnetoencephalography (OPM-MEG) offers significant advantages over traditional systems based on superconducting quantum interference devices, including flexibility and the ability to record brain activity without cryogenic cooling. However, OPM-MEG is highly susceptible to motion artifacts due to its sensitivity to external magnetic field fluctuations.<i>Approach.</i>To address this challenge, we propose an Adaptive Time-varying (ATH) Homogeneous field correction (HFC) method, which integrates time-varying HFC with adaptive filtering to suppress head motion artifacts. The ATH method estimates background magnetic field components induced by head movements and dynamically adjusts filter parameters to minimize discrepancies between measured signals and predicted background fields.<i>Main results.</i>We evaluated the ATH method through simulation studies and median nerve stimulation OPM-MEG experiments, demonstrating its effectiveness in enhancing signal quality and robustness across various experimental conditions.<i>Significance.</i>ATH offers an effective solution for motion artifact suppression in OPM-MEG systems. Its robustness under diverse conditions supports broader application in research and clinical settings.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565584","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":"Enhancing EEG-based sleep staging efficiency with minimal channels through adversarial domain adaptation and active deep learning.","authors":"Roya GhasemiGarjan, Mohammad Mikaeili, Seyed Kamaledin Setarehdan, Arash Saboori","doi":"10.1088/1741-2552/adeec7","DOIUrl":"https://doi.org/10.1088/1741-2552/adeec7","url":null,"abstract":"<p><p>Accurate sleep-stage classification is crucial for advancing both sleep research and healthcare applications. Traditional Deep Learning (DL) and Domain Adaptation (DA) methods often struggle due to the limited availability of labeled data in the target domain and their inability to capture the subtle distinctions between sleep-stage classes, which hampers classification accuracy. To address these limitations, we introduce a novel framework, Adversarial Domain Adaptation with Active Deep Learning (ADAADL). This framework combines adversarial learning with active learning strategies to improve feature alignment and effectively leverage unlabeled data. ADAADL employs two separate sleep-stage classifiers as discriminators, allowing for a more refined consideration of class boundaries during the feature alignment process. Moreover, it incorporates entropy measures alongside cross-entropy loss during training to make better use of the information from unlabeled data. The active learning component (ADL) further enhances performance by iteratively selecting and labeling the most informative data points, thereby reducing annotation efforts and improving generalization to unseen data. Experimental evaluations on three benchmark EEG datasets demonstrate that ADAADL produces robust, transferable features, significantly outperforming existing DA methods in classification accuracy. This research advances sleep-stage classification techniques, offering improved accuracy for real-world applications and contributing to a deeper understanding of sleep dynamics.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621621","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":"Synergic Practice with a Body-Machine Interface: Implications for Individual and Collective Motor Learning.","authors":"Amy Bellitto, Ferdinando A Mussa-Ivaldi, Camilla Pierella, Maura Casadio","doi":"10.1088/1741-2552/adeec9","DOIUrl":"https://doi.org/10.1088/1741-2552/adeec9","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Body-Machine Interfaces (BoMIs) translate human body movements into commands for controlling external devices, such as computer cursors. This process allows researchers to study the development and refinement of inverse models, which generate motor commands necessary for achieving desired movements. Traditionally, motor learning has focused on solo practice, but recent research has shifted towards exploring dyadic tasks, where two individuals practice together. Within dyadic tasks, synergic practice - where partners collaborate towards a shared goal - has shown promise in enhancing performance and reducing stress. However, the impact of contributions of each partner within the synergic practice on individual and collective learning remains underexplored. This study aims to (i) investigate how different levels of contribution during synergic practice affect both individual and collective motor learning, and (ii) assess the impact of these contribution levels on individual performance when returning to solo practice.</p><p><strong>Approach: </strong>40 naïve participants underwent individual practice, dyadic synergic practice, and a final round of individual practice using a BoMI to control a cursor. Participants were classified as high or low contributors based on their participation in the cursor trajectory during dyadic practice. We analyzed how these contribution levels influenced performance, motor strategies, and internal models during and after the dyadic phase.&#xD;Main results.&#xD;During dyadic practice, high contributors maintained motor strategies similar to their initial solo performance, while low contributors showed significant deviations. After returning to solo practice, high contributors retained better task performance, whereas low contributors initially regressed but quickly improved with additional practice, eventually matching high contributors' performance levels.</p><p><strong>Significance: </strong>This understanding holds practical implications for optimizing dyadic practice. Our study sheds light on the influence of synergic practice on subsequent individual motor performance, contributing to a clearer understanding of its advantages and limitations for optimal implementation.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621622","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":"Improved spatial memory for physical versus virtual navigation.","authors":"Shachar Maidenbaum, Vaclav Kremen, Vladimir Sladky, Kai Miller, Jamie Van Gompel, Gregory A Worrell, Joshua Jacobs","doi":"10.1088/1741-2552/ade6aa","DOIUrl":"10.1088/1741-2552/ade6aa","url":null,"abstract":"<p><p><i>Objective</i>. Virtual reality (VR) has become a key tool for researching spatial memory. Virtual environments offer many advantages for research in terms of logistics, neuroimaging compatibility etc. However, it is well established in animal models that the lack of physical movement in VR impairs some neural representations of space, and this is considered likely to be true in humans as well. Furthermore, it is unclear how big the disruptive effect stationary navigation is-how much does physical movement during encoding and recall affect human spatial memory and representations of space? What effect does the fatigue of actually walking during tasks have on participants-will physical movement decrease performance, or increase perception of difficulty?<i>Approach</i>. We utilize Augmented reality (AR) to enable participants to perform a spatial memory task while physically moving in the real world, compared to a matched VR task performed while stationary. Our task was performed by a group of healthy participants, by a group of stationary epilepsy patients, as they represent the population from which invasive human spatial signals are typically collected, and, in a case study, by a mobile epilepsy patient with an investigational chronic neural implant (Medtronic Summit RC + S^TM) streaming real-time continuous hippocampal local field potential data.<i>Main results</i>. Participants showed good performance in both conditions, but reported that the walking condition was significantly easier, more immersive, and more fun than the stationary condition. Importantly, memory performance was significantly better in walking vs. stationary in all groups, including epilepsy patients. We also found evidence for an increase in the amplitude of the theta oscillations associated with movement during the walking condition.<i>Significance</i>. Our findings highlight the importance of paradigms that include physical movement and suggest that integrating AR with movement in real environments can lead to improved techniques for spatial memory research.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12247154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.","authors":"Zhanhui Lin, Xinyu Jiang, Chenyun Dai, Fumin Jia","doi":"10.1088/1741-2552/ade917","DOIUrl":"10.1088/1741-2552/ade917","url":null,"abstract":"<p><p><i>Objective</i>. Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust BCIs are particularly important for physically disabled patients to restore motor ability in outdoor scenarios, where the processing pipeline should be implemented efficiently using constrained computation resources. In this work, we aim to explore the optimal pipeline for mobile BCI decoding.<i>Approach</i>. We comprehensively evaluated the trade-off between the decoding precision, computational efficiency and robustness of diverse decoding algorithms on a combined ECoG dataset of 12 subjects conducting individual finger movement, including partial-least-square and their N-way variants, Bayesian ridge regression, least absolute shrinkage and selection operator, support vector regression, neural networks (NNs) with diverse architectures, and random forest (RF). We further explored the feature optimization technique for selected models by using their inherent model explainability. We also compared the decoding performance of updatable algorithms when the data is split into multiple batches and used sequentially.<i>Main results</i>. The RF model, not valued by previous studies, can achieve the best trade-off between precision and efficiency, achieving an average Pearson's correlation coefficient (<i>r</i>) of 0.466 with only 0.5 K floating-point operations per second (FLOPs) per inference and a model size of 900KiB. We also demonstrate the inherent superior robustness of RF model on corrupted ECoG electrodes, with a>2×decoding precision on noisy signals compared with all state-of-the-art deep NNs. The optimized RF processing pipeline was deployed on a STM32-based embedded platform with only a 15.2 ms computation delay.<i>Significance</i>. In this study, we systematically explored the performance and efficiency of ECoG decoding algorithms in finger movement analysis. The proposed decoding pipeline is implemented on a compact embedded platform to achieve low-latency, power-efficient real-time decoding. This research accelerates the translation of mobile BCI into real-life practices.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513022","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 investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.","authors":"Michael Pritchard, Felipe Campelo, Harry Goldingay","doi":"10.1088/1741-2552/ade1f9","DOIUrl":"10.1088/1741-2552/ade1f9","url":null,"abstract":"<p><p><i>Objective</i>. Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG & EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings.<i>Approach</i>. We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection & Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature.<i>Main results</i>. EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems.<i>Significance</i>. To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies , enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251627","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}