Journal of neural engineering最新文献

{"title":"Decoding speech imagery or just noise?: a symptom of the replicability crisis.","authors":"A Tates, A Matran-Fernandez, S Halder, I Daly","doi":"10.1088/1741-2552/aea294","DOIUrl":"10.1088/1741-2552/aea294","url":null,"abstract":"<p><p><i>Objective.</i>Speech imagery (SI) has emerged as a promising paradigm for brain-computer interface (BCI) control, attracting growing interest due to its intuitive nature-allowing users to interact with the system by internally saying a command. In this study, we investigate the replicability and reproducibility of SI decoding methods. These two aspects are critical in BCI research, where prior literature has highlighted that many studies suffer from incomplete methodological reporting or flawed evaluation procedures, making reproduction difficult. The inherent variability of brain signals further complicates the replication of results. Evaluating the reproducibility of SI decoding approaches is therefore essential to assess the true feasibility of SI as a viable BCI paradigm.<i>Approach.</i>To assess reproducibility, we selected two of the most widely used open-access SI datasets and attempted to reproduce four published decoding pipelines for each dataset. We followed each implementation step-by-step, documented missing or ambiguous information, detailed how we addressed it, and compared our decoding results to those originally reported. To assess replicability, we applied standard decoding pipelines across different time-frequency configurations to three open SI datasets and our own collected dataset. For context and validation, we conducted the same procedure on four publicly available and widely used motor imagery (MI) datasets.<i>Main results.</i>All evaluated SI studies contained some form of missing methodological detail, and others did not include cross-validation procedures. Our reproduction attempts consistently yielded lower classification accuracies than originally reported, with discrepancies ranging from 2 to 39% (). In the replication analysis, we found no consistent time-frequency patterns across SI datasets. Furthermore, only 36% of SI participants achieved classification accuracies above statistical significance thresholds, compared to 91% of the participants in MI datasets.<i>Significance.</i>This is the first comprehensive assessment of both reproducibility and replicability in SI decoding. Our findings raise important concerns about the reliability of current SI research and suggest that the feasibility of SI as a practical BCI paradigm may have been overestimated.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893142","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":"Dose-dependent power and connectivity modulation of low frequency oscillations through transcranial magnetic stimulation in non-human primates.","authors":"Malte R Güth, Nipun D Perera, Gary Linn, Kurt Masiello, Brent Butler, Brian E Russ, Charles E Schroeder, Arnaud Falchier, Alexander Opitz","doi":"10.1088/1741-2552/ae93f6","DOIUrl":"10.1088/1741-2552/ae93f6","url":null,"abstract":"<p><p><i>Objective</i>. Transcranial magnetic stimulation (TMS) is a powerful noninvasive tool for safely modulating neural activity in humans. In particular, the left dorsolateral prefrontal cortex (DLPFC) is a common target site for clinical interventions in disorders such as treatment-resistant depression. Yet, clinical trials investigating the efficacy of TMS often lack neural markers of target engagement of the DLPFC. Local field potentials (LFPs), such as prefrontal theta oscillations, have been implicated in the clinical symptoms of these disorders. However, noninvasive electroencephalography (EEG) recordings in humans are limited by their spatial resolution and challenges of interpreting EEG signals.<i>Approach.</i>In this study, we investigate the effects of single-pulse TMS applied to the left prefrontal cortex in non-human primates on LFPs recorded from intracranial stereo EEG electrodes.<i>Main results.</i>Compared to sham TMS, the intensity of active TMS pulses scaled with LFP power changes in a 1-13 Hz range at contacts close to the stimulation site in the prefrontal cortex (e.g. caudate nucleus, anterior cingulate cortex, insular cortex) as well as contacts that were more distal (e.g. posterior cingulate cortex, temporal lobe). To test how TMS modulates connectivity between these regions, we conducted a phase-based connectivity analysis. TMS pulses initially enhanced and then disrupted connectivity at 1-13 Hz between the stimulation site and other contacts. Connectivity rebounded approximately 1500 ms post-stimulation. Only the initial enhancement in connectivity scaled with TMS intensity.<i>Significance.</i>Our results demonstrate a dose-dependent power modulation of low frequency LFPs across prefrontal, parietal and temporal cortical regions by single pulses. Furthermore, they show that TMS applied over the left prefrontal cortex can enhance and interrupt short- and long-range connectivity. By establishing neurophysiological responses that scale with stimulation intensity, this study identifies target and network engagement measures for evaluation of neuromodulation interventions in the left DLPFC.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13575475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148671655","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":"Information content and interpretation of CSD non-trivially depends on array density.","authors":"Joseph Tharayil, Esra Neufeld, Michael W Reimann","doi":"10.1088/1741-2552/ae93f9","DOIUrl":"https://doi.org/10.1088/1741-2552/ae93f9","url":null,"abstract":"<p><p><i>Objective.</i>We aim to determine whether commonly-used current source density (CSD) estimation techniques provide valid results when used with recordings from the latest generation of high-density arrays and how the density affects signal interpretation.<i>Approach.</i>We first develop an analytic formulation for the contributions of Gaussian current sources to an estimated CSD as a function of current source position and array density. We then simulate local field potential recordings in a biophysically detailed model of a subvolume of rat somatosensory cortex, and compare estimated current source densities to the ground truth current densities averaged over relevant tissue volumes.<i>Main results.</i>Commonly used methods to estimate the CSD can produce spurious results when the inter-electrode spacing is small relative to the width of the current source, confusing sources and sinks. For high-density recording electrodes, the estimated CSD diverges from the volume-averaged ground-truth current distribution that the CSD estimation method attempts to reconstruct. Both confusion of sinks and sources and amplification of local current fluctuations play a role in this divergence. Careful choice of CSD reconstruction parameters can be used to preferentially reveal the information content on a local or higher-level scale.<i>Significance.</i>Our results shed light on high-spatial-frequency oscillations observed in estimated current source densities from<i>in vivo</i>experiments. Care must be taken when choosing a reconstruction method and interpreting the results from high density arrays, but proper parameter tuning allows to focus on specific information-of-interest. In addition, it was found that detailed network models, including active conductances and background noise, can be necessary to accurately simulate high-frequency current fluctuations.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"23 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972293","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":"Brain-computer interface clinical trial design considerations and clinical outcome assessments in pivotal studies: a summary of the 11th BCI society meeting 2025 workshop.","authors":"Abbey Sawyer, Cristin G Welle, Jennifer French, Jessica Kelemen, Luke Bashford, Daniel Kramer, David McMullen, Robert Franklin, Matthew R Angle, Dustin Herrmann, Thomas Oxley, Jacqueline Dister, Nikole Chetty, Erin M Radcliffe, Jakub Kowalczyk, Rohit Bose, Juhi Farooqui, Leigh R Hochberg","doi":"10.1088/1741-2552/ae9eee","DOIUrl":"10.1088/1741-2552/ae9eee","url":null,"abstract":"<p><p><i>Objective.</i>The Brain Computer Interface (BCI) Society Meeting 2025 held a workshop in collaboration with the Implantable BCI Collaborative Community (iBCI-CC) to discuss the selection, development and validation of clinical trial outcome assessments (COAs) for pivotal studies of BCIs. The iBCI-CC Clinical Study Endpoints Workgroup aims to build consensus on a COA framework that can meet the demands of emerging iBCI trials.<i>Approach.</i>The workshop brought together diverse stakeholders from the iBCI-CC community and beyond, including industry, academic and government institutions to engage in pre-competitive collaborative discussion.<i>Main results.</i>Through presentations, panel discussions and breakout groups, workshop participants highlighted meaningful aspects of health (MAHs) relevant to iBCI users, clarified the need to define corresponding concepts of interest (COIs) and to identify or adapt COAs capable of capturing both functional impact and real-world use. Key challenges highlighted during the workshop are patient heterogeneity, the lack of widely validated and fit-for-purpose COAs, and the need to capture meaningful outcomes in home and daily-life environments. Lessons drawn from trials such as ADAPT-PD underscore the value of capturing device performance in home and daily-life settings, highlighting the need for context-sensitive and flexible metrics that reflect patient priorities.<i>Significance.</i>This workshop lays a foundation for the iBCI-CC Clinical Study Endpoints Workgroup to establish a transparent process for identifying MAHs and COIs that are suitable for specific iBCI technologies, patient-informed, and conducive to regulatory approval and reimbursement. By combining rigorous, quantitative assessment with patient-informed goals, iBCI clinical trials can advance toward safe, effective, and accessible neurotechnologies that enhance how a person with a severe motor impairment feels, functions and survives.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148835932","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":"SEDAT: a hybrid tokenizer for large EEG models.","authors":"Muhammad Zulkifal Aziz, Zhuo Yue, Huang Binwen, Yu Xiaojun","doi":"10.1088/1741-2552/aea0ba","DOIUrl":"10.1088/1741-2552/aea0ba","url":null,"abstract":"<p><p><i>Objective.</i>The fidelity of neural representations learned by large EEG foundation models depends on how raw brain signals are tokenized. Existing methods suffer from arbitrary temporal boundaries misaligned with neural state transitions, neglecting inter-channel spatial information, and fixed segmentation criteria that fail to generalize across heterogeneous EEG paradigms.<i>Approach.</i>This study proposes the squeeze-and-excitation (SE)-data-adaptive Gaussian average filtering (DAGAF) adaptive tokenizer (SEDAT), a hybrid framework integrating SE-based spatial aggregation, DAGAF-based signal decomposition, instantaneous-frequency-guided adaptive segmentation, and Fourier-domain resampling into a single computationally efficient pipeline. SEDAT is evaluated across 10 heterogeneous EEG datasets spanning motor imagery, mental imagery, P300, slow cortical potentials, sleep staging, epilepsy, emotions recognition and Alzheimer diagnosis paradigms, using four large foundation models: LaBraM, EEGFormer, EEGPT, and NeuroGPT. It is benchmarked against five competitive baselines: fixed-length windowing (FLW), context segmentation, linear predictive coding-based tokenization, TFM-Tokenizer, and source informed segmentation.<i>Main results.</i>SEDAT achieves classification improvements of up to 15.3% over FLW and 1.2%-4.6% over the second-best method, with all comparisons reachingafter Benjamini-Hochberg correction. Token quality analysis confirms substantially improved feature separability, with Silhouette scores of 0.81-0.85 versus 0.33-0.48 for rigid baselines.<i>Significance.</i>Withcomplexity, SEDAT explores new applications for SE and DAGAF as tokenizers and provides a physiologically grounded and computationally practical tokenization solution for large-scale EEG foundation models.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868621","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":"40 Hz noninvasive transcranial ultrasound stimulation modulates multiscale nonlinear dynamics of hippocampal CA1 neural oscillations of Alzheimer's mouse model.","authors":"Jiamin Chen, Hui Ji, Yi Yuan","doi":"10.1088/1741-2552/ae9eed","DOIUrl":"10.1088/1741-2552/ae9eed","url":null,"abstract":"<p><p><i>Objective.</i>Aberrant nonlinear dynamics of hippocampal neural oscillations are a hallmark of early network dysfunction in Alzheimer's disease (AD), with critical roles in memory processing. Although transcranial ultrasound stimulation (TUS) has shown cognitive benefits in AD models and patients, its effects on the nonlinear dynamics of hippocampal CA1 oscillations and their multiscale organization remain unclear.<i>Approach.</i>Here, we applied 40 Hz noninvasive TUS to the hippocampal CA1 region of AD mouse model and recorded local field potentials before, during, and after stimulation. Analyses integrated nonlinear dynamical metrics, power spectral dependencies, cross-frequency coupling, and event-level oscillatory features.<i>Main results.</i>We found that (1) 40 Hz TUS decreases complexity and irregularity of CA1 activity, and enhances dynamical activity and long-range temporal correlations in broadband and high-frequency ranges, increases complexity in the theta and beta bands. (2) TUS also significantly reduced theta-gamma and intra-gamma phase-amplitude coupling, while producing frequency-specific changes in the rate, amplitude, duration, and temporal organization of theta, gamma, and ripple events. (3) Furthermore, the relationship between nonlinear dynamics and power spectral density was modulated across stimulation phases.<i>Significance.</i>40 Hz TUS can modulate multiscale nonlinear dynamics of neural oscillations in hippocampal CA1 of Alzheimer's mouse model, which lays a theoretical foundation for the clinical intervention of AD via ultrasound stimulation.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148835925","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":"DynaSPARC: a dynamic single-trial P300 paradigm for assessing motor response capability in autism spectrum disorder.","authors":"Hongxuan Chen, Zhuo Zhou, Dan Chen, Yaodong Wang, Tengfei Gao, Xiaodi Liu, Jingying Chen","doi":"10.1088/1741-2552/ae9ef0","DOIUrl":"https://doi.org/10.1088/1741-2552/ae9ef0","url":null,"abstract":"<p><p><i>Objective.</i>EEG-based assessment of motor response capability is critical for understanding the heterogeneous cognitive profiles of children with autism spectrum disorder (ASD), e.g. typically P300 event assessment. However, mainstream methods generally struggle to address the entangled trial-to-trial variability and temporal instability of P300 signatures in ASD, fixed time windows or trial-averaging primarily effective only for typically developing subjects.<i>Approach.</i>To overcome this limitation, this paper introduces<b>DynaSPARC</b>(<b>Dyna</b>mic<b>S</b>ingle-trial<b>P</b>300<b>A</b>ssessment of<b>R</b>esponse<b>C</b>apability), a framework based on the premise that behavioral reaction time () provides a latent regulation for inferring trial-specific cognitive dynamics.<b>DynaSPARC</b>enables reliable assessment of motor response capability in ASD through: (1)<b>Dynamic Temporal Windowing</b>, which uses a nonlinear mapping of standardizeddifferences to adaptively parameterize the start and length of the P300 window for each trial within a physiologically constrained range (e.g. 300-800 ms); (2)<b>Collaborative Spatio-temporal Attention</b>, which employs a learnable temporal filter to pinpoint P300 latency and a knowledge-guided channel weighting scheme for a reliable reconstruction of the P300 signature.<i>Main results.</i>Evaluation on an EEG dataset from 67 children (29 with ASD vs 38 typically developing) through a motor-cognitive task demonstrates that<b>DynaSPARC</b>achieves superior performance over fixed-window methods: (1) A more temporally localized and spatially plausible single-trial P300 was obtained, and a substantially stronger-peak-latency coupling was preserved (Spearman= 0.61,= 1.33vs= 0.11, p = 0.391), consistent with more stable peak-latency estimates (reduced jitter); (2) The peak scalp topography showed a more canonical, spatially focused centro-parietal positivity; (3) The classification accuracy,<i>F1</i>score, recall, and precision reached 84.77%, 78.43%, 84.03%, and 76.14%, respectively.<i>Significance.</i>This work establishes a new paradigm for EEG-based assessment, moving beyond static averaging to model the dynamic interplay between neural latency and behavioral output.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"23 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148914751","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":"DRDNet: a dual-view representation decoupling network for handwriting imagery EEG classification.","authors":"Zhihao Li, Fan Wang, Haichen Lu, Rongzhang Luo, Tianwen Li, Lei Zhao, Yunfa Fu","doi":"10.1088/1741-2552/ae9bef","DOIUrl":"10.1088/1741-2552/ae9bef","url":null,"abstract":"<p><p><i>Objective</i>. Handwriting imagery (HI) based on electroencephalography (EEG) offers a non-invasive route to text input and complex intention expression for brain-computer interfaces (BCIs). However, HI EEG decoding is challenged by low signal-to-noise ratios, non-stationarity, cross-session distribution shifts, and the coexistence of continuous temporal trends and local high-response patterns.<i>Approach</i>. We propose a Dual-View Representation Decoupling Network (DRDNet) for HI EEG classification under within-subject cross-session and cross-subject evaluation settings. DRDNet first constructs two complementary temporal views from spatial EEG features using average and max pooling, corresponding to smooth trend-oriented and salient response-oriented representations. These views are then modeled by a bidirectional Mamba encoder and a Transformer encoder, respectively, and adaptively integrated through a time-step-level dynamic fusion mechanism followed by long short-term memory-based temporal aggregation. The method is evaluated on two tasks from a public HI EEG dataset: Chinese character stroke HI (CCSHI) and pinyin single-vowel HI (SVHI).<i>Main results</i>. DRDNet achieved average accuracies of 67.74% and 62.51%, with Cohen's kappa scores of 0.5968 and 0.5502, on CCSHI and SVHI, respectively. Under the cross-subject setting, DRDNet also achieved the best average accuracies of 62.54% and 54.40% on CCSHI and SVHI, respectively. DRDNet outperformed seven representative EEG decoding baselines under both evaluation settings. Confusion matrices, feature visualization, ablation studies, structural variant analysis, and complexity evaluation further showed improved feature separability and a favorable balance between decoding performance and computational efficiency.<i>Significance</i>. The results indicate that decoupling HI EEG into complementary temporal views and matching them with heterogeneous temporal encoders provides an effective representation learning strategy for robust non-invasive handwriting BCI decoding.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803765","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":"Unsupervised domain transfer: overcoming signal degradation in sleep monitoring by increasing scoring realism.","authors":"Mohammad Ahangarkiasari, Andreas Tind Damgaard, Casper Haurum, Kaare B Mikkelsen","doi":"10.1088/1741-2552/ae9f96","DOIUrl":"10.1088/1741-2552/ae9f96","url":null,"abstract":"<p><p>. Investigate whether hypnogram 'realism' can be used to guide an unsupervised method for handling arbitrary types of signal degradation in mobile sleep monitoring.. Combining a pretrained, state-of-the-art U-sleep model with a 'discriminator' network, we align features from a target domain with a feature space learned during pretraining. To test the approach, we distort the source domain with realistic signal degradations, investigating how well the method can adapt to different types of degradation. We compare the performance of the resulting model with best-case models designed in a supervised manner for each type of transfer.. Depending on the type of distortion, we find that the unsupervised approach can increase Cohen's kappa with as little as 0.03 and up to 0.29, and that for all transfers, the method does not decrease performance. However, the approach never quite reaches the estimated theoretical optimal performance, and when tested on a real-life domain mismatch between two sleep studies, the benefit was insignificant.. 'Discriminator-guided fine-tuning' is an interesting approach to handling signal degradation for 'in the wild' sleep monitoring, with some promise. In particular, what it says about sleep data in general is interesting. However, more development will be necessary before using it in production. Index terms: transfer learning, sleep scoring, deep learning, adversarial learning.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842587","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":"What matters in next-generation implanted neurotechnology design? Perspectives from individuals with treatment-refractory depression.","authors":"Juhi Farooqui, Jonah Toennies, Landan M Mintch, Jonathan Nelson, Benjamin Yudkoff, Mario Cristancho, Katherine Scangos, Casey H Halpern, Jacob T Robinson, Anna Wexler","doi":"10.1088/1741-2552/ae93f8","DOIUrl":"10.1088/1741-2552/ae93f8","url":null,"abstract":"<p><p><i>Objective</i>. Major financial investments in implanted neurotechnology have fueled a wave of innovation in device design, implant procedures and therapy delivery methods. These innovations are often presented as making implanted neurotechnologies more appealing to patients. However, it remains unclear whether these innovations meaningfully impact patients' decision-making about adoption. The present study aims to examine how next-generation neurotechnology innovations shape prospective decision-making about adoption among patients with treatment-refractory depression (TRD), a key projected target population for these neurotechnologies.<i>Approach</i>. We identified nine aspects of next-generation neurotechnology innovation by reviewing public-facing materials from leading implanted neurotechnology companies. We then conducted interviews with 27 participants with TRD as well as six participants who currently have a deep brain stimulation implant for depression to assess their familiarity with and perceptions of neurotechnology, as well as their perspectives on next-generation innovations in implanted neurotechnologies for depression.<i>Main results</i>. While participants reported only moderate familiarity with implanted neurotechnology, most were willing to consider it as a treatment option if their depression was sufficiently severe. We identified four aspects of innovation that strongly impacted participants' decision-making about adoption: flexible therapy delivery, implant approach, internal footprint, and brain penetration. The remaining aspects (length of implant procedure, contact with brain, external visibility, device size and depth in skull) were seen as secondary considerations rather than decision-driving factors.<i>Significance</i>. Aspects of device design and therapy design that represent substantial patient burden or major intrusions into the body were most impactful for patients' prospective decisions about implanted neurotechnologies. Meanwhile, other aspects that are often emphasized by the industry-such as cosmetic invisibility and length of the implant procedure-were given less weight by the interviewed patients. These findings can help guide patient-centered priority-setting in device design in the TRD context.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148671802","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}