Journal of neural engineering最新文献

{"title":"The effect of different observation modalities in motor imagery: immersive virtual reality produces stronger desynchronization than video and real-world presentations.","authors":"Daniel Carrillo-Martín, Mateo Fortich-Mora, J Ignacio Serrano, M Dolores Del Castillo","doi":"10.1088/1741-2552/aeabf4","DOIUrl":"10.1088/1741-2552/aeabf4","url":null,"abstract":"<p><p><i>Objective</i>. The combination of action observation and motor imagery (AO + MI) has emerged as a promising neurorehabilitation strategy to promote sensorimotor plasticity without real movement, making it particularly valuable for individuals whose motor capacity is limited or absent. Improving AO + MI protocols requires understanding whether different observation stimuli engage the sensorimotor system to varying degrees, as the quality of cortical activation during each training trial may directly influence cumulative neuroplastic outcomes. The growing accessibility of immersive technologies such as virtual reality head-mounted displays and robotic agents opens new possibilities for enriching the observation component of AO + MI, yet the neurophysiological impact of these modalities relative to conventional video approaches remains largely unexplored. Therefore, this study aimed to determine how the observation modality influences the activation of the sensorimotor cortex during synchronous AO + MI.<i>Approach</i>. This study investigated the effects of three observation modalities: immersive virtual reality (iVR), two-dimensional video and real-world observation of a robotic agent, on sensorimotor cortical engagement, using event-related desynchronization (ERD) as a neurophysiological marker. Electroencephalographic signals were recorded from fifteen healthy participants across three sessions, one per modality, while they observed a humanoid robot performing arm movements and simultaneously engaged in motor imagery of the same movements. ERD was computed for alpha and beta frequency bands over eight electrodes covering frontocentral, central, and centroparietal regions.<i>Main Results</i>. Statistical analyses revealed significant modality-dependent differences in sensorimotor cortical engagement across multiple frequency bands, electrode locations, and movement types. iVR consistently elicited the strongest desynchronization, with robust effects in the beta band over frontocentral and right centroparietal regions, and in the alpha band over the right central motor cortex.<i>Significance</i>. These results demonstrate that the modality of action observation significantly shapes sensorimotor cortical engagement during AO + MI. Our findings also support the integration of iVR as a neurophysiological grounded tool for optimizing AO + MI-based neurorehabilitation protocols and brain-computer interface applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032343","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":"Towards maximizing optical access through Smart Dura.","authors":"Sergio Montalvo Vargo, Nari Hong, Gaku Hatanaka, Vishal Jain, Hefu Pan, Ruikang K Wang, Wyeth Bair, Azadeh Yazdan-Shahmorad, Maysamreza Chamanzar","doi":"10.1088/1741-2552/ae8eb3","DOIUrl":"10.1088/1741-2552/ae8eb3","url":null,"abstract":"<p><p><i>Objective.</i>To overcome the limitations of optical access caused by opaque electrodes in multimodal neural interfaces used in large-animal models, and to enable scalable, high-density electrophysiology with unobstructed optical imaging and optogenetic stimulation.<i>Approach.</i>In this work, we redesigned our previously demonstrated multimodal cortical surface array, the Smart Dura, by incorporating transparent indium tin oxide (ITO) electrodes. ITO provides good electrical conductivity, while providing high levels of transparency. We discuss the design, microfabrication, benchtop characterization and<i>in vivo</i>demonstration of transparent Smart Dura for multimodal brain interfacing.<i>In vivo</i>experiments were designed to show the feasibility of simultaneous electrophysiological recording with localized functional optical imaging through transparent Smart Dura in response to visual stimulation.<i>Main results.</i>The implementation of ITO electrodes increased the effective optical access of Smart Dura to 100%, eliminating obstruction from traditional metal electrodes. Bench-top characterization confirmed the electrical and optical performance of the transparent electrodes.<i>In vivo</i>results demonstrate direct visual access underneath the ITO electrodes for optical recording and perturbation with co-located simultaneous high-fidelity electrophysiological recording, validating the multimodal capabilities of the transparent Smart Dura.<i>Significance.</i>The transparent Smart Dura design removes major barriers to optically integrated, high-density electrophysiology in large-animal models and the number and density of ITO electrodes on Smart Dura can be increased without compromising the optical access. This platform advances multimodal neural interfacing in non-human primates and provides unique opportunities to study brain function in health and disease and design therapeutics that can readily translate to humans.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"23 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120967","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":"Influence of out-of-plane electric fields in an ex vivo model of transcranial electrical stimulation.","authors":"Yuhyun Lee, Vishal Jain, Yuxin Guo, Maysamreza Chamanzar, Pulkit Grover, Mats Forssell","doi":"10.1088/1741-2552/aeb04a","DOIUrl":"https://doi.org/10.1088/1741-2552/aeb04a","url":null,"abstract":"<p><strong>Objective: </strong>Electrical stimulation is the most commonly used modality for neuromodulation, with a number of clinical and scientific applications. Although the basic mechanisms of neural activation by electrical stimulation are well understood, important questions remain regarding neural responses to diffuse electric fields such as those generated by transcranial electrical stimulation. For such diffuse fields, the orientation of the electric field with respect to the neurons being stimulated matters, but previous ex vivo brain slice studies-commonly used to study how neurons respond to external electric fields-have only considered electric fields applied in the plane of the slice.</p><p><strong>Approach: </strong>We used two electrode arrays-one oriented parallel and the other perpendicular to coronal brain slices-to generate electric fields oriented orthogonally to the slice as well as along the cortical column. We measured responses in Layer 5 of the somatosensory cortex using several 2-electrode stimulation configurations, while also varying the distance between the electrodes and the slice.</p><p><strong>Main results: </strong>Electrode patterns generating a large electric field component orthogonal to the slice evoked the largest neural responses at every electrode-slice distance. Moreover, neural thresholds were > 5× lower for electric fields orthogonal to the slice compared to electric fields along the cortical column.</p><p><strong>Significance: </strong>These results provide a better understanding of the brain slice as a model for noninvasive stimulation and could lead to new electrode configurations for efficient transcranial neural stimulation.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120992","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":"A statistical mixture-of-experts framework for EMG artifact removal in EEG: empirical insights and a proof-of-concept application.","authors":"Benjamin J Choi, Griffin Milsap, Clara A Scholl, Francesco Tenore, Mattson Ogg","doi":"10.1088/1741-2552/aea4f8","DOIUrl":"10.1088/1741-2552/aea4f8","url":null,"abstract":"<p><p><i>Objective.</i>Effective control of neural interfaces is limited by poor signal quality. While neural network-based electroencephalography (EEG) denoising methods for electromyogenic (EMG) artifacts have gained traction in recent years, existing models perform suboptimally in settings with high noise. Since neural interfaces rely almost universally on some form of signal processing, uncovering effective algorithmic insights into EEG denoising is crucial for future success.<i>Approach.</i>To address the shortcomings of current machine learning (ML)-based denoising algorithms, we present a single-channel signal filtration algorithm driven by a new mixture-of-experts (MoEs) framework. Our algorithm leverages three new statistical insights into the EEG-EMG denoising problem: (1) EMG artifacts can be partitioned into quantifiable types to aid downstream MoE classification, (2) local experts trained on narrower signal-to-noise ratio ranges can achieve performance increases through specialization, and (3) correlation-based objective functions, in conjunction with rescaling algorithms, can enable faster convergence in a neural network-based denoising context.<i>Main results.</i>We empirically demonstrate these three insights into EMG artifact removal and use our findings to create a new downstream MoE denoising algorithm consisting of convolutional and recurrent neural networks. We tested all results on a major benchmark dataset (EEGdenoiseNet) collected from 67 subjects. We found that our MoE denoising model achieved competitive overall performance with contemporary ML denoising algorithms and strong lower bound performance in high noise settings. We further validated the framework on the SEED EEG corpus, observing statistically significant downstream classification gains under high-noise contamination.<i>Significance.</i>These preliminary results highlight the promise of our MoE framework for enabling advances in EMG artifact removal in EEG, especially in high noise settings. Further experimentation will be necessary to assess our MoE framework on a wider range of test cases and explore its downstream potential to unlock more effective neural interfaces.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148922125","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":"BRAINet: a brain-region-aware interaction network for EEG-based diagnosis of disorders of consciousness.","authors":"Haoxiang Chen, Sha Zhao, Jie Yu, Jiquan Wang, Yumeng Bai, Chuan Xu, Shijian Li, Benyan Luo, Gang Pan","doi":"10.1088/1741-2552/aea7db","DOIUrl":"10.1088/1741-2552/aea7db","url":null,"abstract":"<p><p><i>Objective.</i>Reliable assessment and stratification of disorders of consciousness (DOC) is essential for patient care and clinical treatment planning. Electroencephalography (EEG) provides a non-invasive approach to measure neural activity and has shown promise in DOC assessment. However, most existing EEG-based approaches focus on binary unresponsive wakefulness syndrome (UWS)/minimally conscious state (MCS) classification and often process EEG channels as a whole, without explicitly modeling anatomical brain-region organization. In this study, our goal is to distinguish among UWS, minimally conscious state minus (MCS), and minimally conscious state plus (MCS) using resting-state EEG signals.<i>Approach.</i>We propose BRAINet, a brain-region-aware EEG framework for three-class DOC classification. BRAINet partitions EEG channels into five anatomical brain regions, extracts region-specific spatiotemporal and spectral features, models cross-region interactions using a Transformer-based attention module, and fuses the learned representations with approximate entropy features for final classification. We evaluated BRAINet on a clinical resting-state EEG dataset comprising 22 UWS, 24 MCS, and 15 MCSpatients using patient-wise five-fold cross-validation and comparisons with representative machine-learning and deep-learning baselines. Statistical comparisons were based on paired patient-level out-of-fold (OOF) predictions.<i>Main results.</i>BRAINet achieved the highest numerical performance among the compared methods. Across the five folds, its mean balanced accuracy (BA) was 54.07% at the epoch level and 59.89% at the subject level. Based on pooled patient-level OOF predictions, BRAINet achieved significantly higher BA than the best-performing baseline, Conformer (two-sided paired permutation test, Holm-adjusted).<i>Significance.</i>These results suggest that brain-region-aware EEG modeling may provide useful information for fine-grained UWS/MCS/MCSclassification. BRAINet provides an interpretable framework for exploring region-specific EEG representations in DOC and may support future studies on patient stratification and prognostic assessment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978847","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":"Influences of design aspects on recording functionality of cuff-type peripheral nerve interfaces.","authors":"Vera A Oppelt, Emma Mueller, Fabian Pfeiffer, Seline Mahler, Ronny Pfeifer, Martin Schuettler, Thomas Stieglitz","doi":"10.1088/1741-2552/aea36d","DOIUrl":"10.1088/1741-2552/aea36d","url":null,"abstract":"<p><p><i>Objective.</i>High-quality recording of peripheral nerve signals is a critical requirement for implantable neural interfaces, yet the influence of contact configuration and electrical insulation on cuff electrode recording quality remains poorly understood.<i>Approach.</i>Using a physical nerve model<i>in vitro</i>, the transfer function (TF) of commercially available split-cylinder and spiral cuff electrodes was measured for systematically varied contact configurations, including contact size, spacing, and size ratio of cathode to anode, as well as degree of electrical insulation. To derive single-fiber and compound action potentials, the TF was convolved with modeled action currents, enabling direct comparison of recording quality across designs.<i>Main results.</i>Contact edge spacing was found to be more decisive than contact surface area for recording amplitude, regardless of contact segmentation. In quasi-tripolar configurations, common mode suppression additionally reduced sensitivity to geometric variations. Both the length of the longitudinal insulation and the integrity of the electrode seal were found to strongly influence recording amplitude, with complete removal of the seal reducing amplitude by approximately 50%, outweighing all geometric design parameters combined.<i>Significance.</i>These findings establish electrical insulation integrity as the primary design criterion for cuff electrode recording performance, shifting focus away from contact geometry alone, and provide a practical framework for the development of next-generation peripheral nerve interfaces.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908480","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 open-set learning framework for EEG-based sleep stage classification.","authors":"Qiunan Feng, Chang Li, Hu Peng, Heyuan Qiao, Rencheng Song, Xun Chen","doi":"10.1088/1741-2552/aea59e","DOIUrl":"10.1088/1741-2552/aea59e","url":null,"abstract":"<p><p><i>Objective.</i>Existing sleep stage classification methods generally rely on a closed set assumption. They assume all sleep categories are predefined, which overlooks unknown sleep patterns encountered in real world electroencephalogram (EEG) analysis. This study overcomes this constraint by identifying both known and unknown sleep stages.<i>Approach.</i>We developed a two stage open set learning framework for EEG sleep staging. First, an adaptive Gaussian method models each known category to compute Gaussian scores, utilizing class specific quantile adaptive thresholds to separate known from unknown samples. Second, we integrate a trainable decision boundary OpenMax (TDBOM) module. TDBOM optimizes decision boundaries during training to suppress samples with large boundary deviations, while calibrating normal samples to yield reliable probability estimates.<i>Main results.</i>Evaluated on three public EEG datasets, our framework accurately classifies known sleep stages and reliably detects unknown categories, significantly boosting open-set recognition performance.<i>Significance.</i>This work provides a practical and robust solution for EEG-based sleep stage classification under open-set conditions, facilitating more reliable sleep analysis and supporting potential applications in real-world clinical monitoring and sleep disorder assessment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932250","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":"Dynamical network models predict spectral response to single-pulse electrical stimulation.","authors":"Helen E Brinyark, Caila A Coyne, Nicholas Reyes, Benjamin C Cox, Arie Nakhmani, Rachel J Smith","doi":"10.1088/1741-2552/aea59c","DOIUrl":"10.1088/1741-2552/aea59c","url":null,"abstract":"<p><p><i>Objective.</i>Drug-resistant epilepsy can be treated by surgically removing the seizure onset zone (SOZ), but localizing this zone is challenging because no clinically accepted biomarker of the SOZ exists. A novel computational biomarker, neural resonance, utilizes control theory to model the brain's response to stimulation. However, model validation is limited, and it is necessary to further contextualize the relationship between the model's prediction and the brain's true response to stimulation.<i>Approach.</i>Single-pulse electrical stimulation (SPES) provides an opportunity to bridge this gap as Bode plots derived from dynamical network models built from SPES data can be directly compared to the spectral response of the SPES-evoked waveform, the cortico-cortical spectral response (CCSR). In this study, we tested whether neural resonance, predicted by dynamical network models, reflects the SPES-evoked CCSR.<i>Main results.</i>Using SPES data from 33 patients, we constructed dynamical network models and compared the model-predicted magnitude and frequency behavior (Bode plot) to the CCSR curve measured for each patient. Across patients, Bode plots correlated best with the CCSR curves taken at the time of the N₂component of the evoked response and taken as the average post-stimulus CCSR response (median Pearson correlation coefficient,0.45 and 0.60, respectively), and significantly better than CCSR curves from randomly selected brain regions (Wilcoxon signed-rank test,<i>p</i><). We also found that resonant frequencies in the Bode plot were significantly more co-expressed in CCSR curves coinciding with the time of the N1 and N₂components of the evoked response, features associated with early direct connectivity and delayed indirect connectivity, respectively (Wilcoxon rank-sum test, both<i>p</i><).<i>Significance.</i>These findings validate the use of dynamical network models to predict neural connectivity and further support the use of neural resonance as a biomarker helping to localize the SOZ through guiding stimulation to induce seizures.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932196","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":"Recognition of<i>ex vivo</i>spike response of retina using a convolution-attention combined framework.","authors":"Junchi Wang, Pengxiang Li, Lei Zhang, Wenwei Shao, Xin Zhang, Liqun Chen","doi":"10.1088/1741-2552/aea4f6","DOIUrl":"10.1088/1741-2552/aea4f6","url":null,"abstract":"<p><p><i>Objective.</i>This study aimed to develop and evaluate a compact spike decoding framework for classifying visual stimulus from retinal multi-channel electrode array (MEA) recordings that may have the problem of poor electrode-tissue coupling. Specifically, we sought to establish a convolution-attention framework that can extract spike features and capture longer-range temporal dependencies in visually evoked retinal activities.<i>Approach.</i>MEA recordings from<i>ex vivo</i>mouse retinas were acquired under controlled color and shape stimulation paradigms. Spike trains were converted into spike-count sequences and used to train an artificial neural network framework combining convolutional neural network layers with temporal multi-head attention modules. Convolutional layers extracted local temporal features, while the attention modules facilitated broader temporal relationships across the response sequence.<i>Main results.</i>The proposed convolution-attention framework achieved good performance across both color and shape tasks. The proposed framework reached 93.250% accuracy in color classification and 73.161% accuracy in the shape classification task. These results show that the proposed framework can decode visually evoked retinal spike responses under different stimulus conditions and retains stable performance across tasks with different levels of complexity.<i>Significance.</i>This study presents a compact decoding framework for retinal spike trains and provides a potential computational backbone for spike-based neural decoding in bio-electronic interfaces. By improving stimulus classification from retinal neural activity, the proposed model may provide a cornerstone to support future visual neuroengineering and bio-digital convergence.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148922087","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":"Jogging imagery congruent with avatar gait is associated with enhanced avatar ownership during multimodal BCI control.","authors":"Taiga Seri, Seitaro Iwama, Junichi Ushiba","doi":"10.1088/1741-2552/aea4f5","DOIUrl":"10.1088/1741-2552/aea4f5","url":null,"abstract":"<p><p><i>Objective.</i>Multimodal brain-computer interfaces (BCIs) integrate neural and non-neural signals to enable intuitive interaction with external devices. However, BCI controllability is still predominantly evaluated based on neural signal strength, leaving the role of embodied self-experience largely unclear. Here, we investigated how kinesthetic congruence between motor imagery (MI) and avatar gait under BCI control contributes to subjective embodiment.<i>Approach.</i>Participants navigated a virtual avatar with a jogging gait through a curved course using a multimodal BCI. Locomotion was controlled by MI-related sensorimotor rhythm event-related desynchronization (SMR-ERD) derived from scalp electroencephalography, whereas movement direction was controlled by eye gaze. To manipulate kinesthetic congruence between MI and avatar gait, participants performed imagery of jogging (congruent condition) or sustained right-hand opening (incongruent condition). Subjective embodiment toward the controlled avatar was assessed using questionnaires.<i>Main results.</i>Participants reported significantly greater ownership in the congruent condition than in the incongruent condition. Although jogging imagery elicited weaker MI-related SMR-ERD than hand-opening imagery, no statistically significant condition differences were detected in course completion rate or time remaining upon completion.<i>Significance.</i>These findings suggest that, in multimodal BCIs, neural signal strength and embodied experience may dissociate, and that imagery-action congruence should be considered alongside conventional decoding metrics. By showing that action-congruent imagery enhances avatar ownership despite weaker neural signatures, this study motivates a shift from signal-centric toward context-aware BCI design for more intuitive and embodied interaction.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148922157","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}