Journal of neural engineering最新文献

{"title":"The speech reception threshold can be estimated using EEG electrodes in and around the ear.","authors":"Heidi B Borges, Johannes Zaar, Emina Alickovic, Christian B Christensen, Preben Kidmose","doi":"10.1088/1741-2552/ae00f3","DOIUrl":"10.1088/1741-2552/ae00f3","url":null,"abstract":"<p><p><i>Objective.</i>Previous studies have demonstrated that the speech reception threshold (SRT) can be estimated using scalp electroencephalography (EEG), referred to as SRT_neuro. The present study assesses the feasibility of using ear-EEG, which allows for discreet measurement of neural activity from in and around the ear, to estimate the SRT_neuro.<i>Approach.</i>Twenty young normal-hearing participants listened to audiobook excerpts at varying signal-to-noise ratios (SNRs) whilst wearing a 66-channel EEG cap and 12 ear-EEG electrodes. A linear decoder was trained on different electrode configurations to estimate the envelope of the audio excerpts from the EEG recordings. The reconstruction accuracy was determined by calculating the Pearson's correlation between the actual and the estimated envelope. A sigmoid function was then fitted to the reconstruction-accuracy-vs-SNR data points, with the midpoint of the sigmoid serving as the SRT_neuroestimate for each participant.<i>Main results.</i>Using only in-ear electrodes, the estimated SRT_neurowas within 3 dB of the behaviorally measured SRT (SRT_beh) for 6 out of 20 participants (30%). With electrodes placed both in and around the ear, the SRT_neurowas within 3 dB of the SRT_behfor 19 out of 20 participants (95%) and thus on par with the reference estimate obtained from full-scalp EEG. Using only electrodes in and around the ear from the right side of the head, the SRT_neuroremained within 3 dB of the SRT_behfor 19 out of 20 participants.<i>Significance.</i>These findings suggest that the SRT_neurocan be reliably estimated using ear-EEG, especially when combining in-ear electrodes and around-the-ear electrodes. Such an estimate can be highly useful e.g. for continuously adjusting noise-reduction algorithms in hearing aids or for logging the SRT in the user's natural environment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984083","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":"Inter-ictal spike rates are not modulated by anti-seizure medication taper in the epilepsy monitoring unit: a tale of two confounders.","authors":"Nina Jasmine Ghosn, Katherine Walsh, Kevin Xie, Carlos Aguila, Akash Pattnaik, Devin Ma, Abba Krieger, Erin Conrad, Brian Litt","doi":"10.1088/1741-2552/ae0521","DOIUrl":"https://doi.org/10.1088/1741-2552/ae0521","url":null,"abstract":"<p><strong>Objective: </strong>New implantable and wearable devices hold great promise to help patients manage their seizure disorders. One proposed application is measuring the rate of interictal epileptiform discharges as a biomarker of medication levels and seizure risk. This study aims to determine whether interictal epileptiform spike rates (spikes) are independently associated with anti-seizure medication (ASM) levels and evaluate whether spike rates are a reliable biomarker for ASM levels. &#xD;Approach. We conducted a retrospective analysis of 69 patients with drug resistant epilepsy undergoing intracranial EEG monitoring during ASM taper in the Epilepsy Monitoring Unit (EMU). An automated spike detection algorithm, validated seizure annotations, and a model of ASM load were used to assess the relationship between spike rates, ASM level, state of consciousness, and seizure timing using linear mixed effects models. Event related analysis and seasonal autoregressive models were applied to evaluate both short-term and longer-term effects of ASMs on spike rates, respectively.&#xD;Main results. Spike rates were found to increase following seizures, during sleep, and in patients experiencing early seizures, with no significant association between ASM load and spike rates after controlling for these confounders. Initial models without controls showed a positive association between ASM load and spike rates. However, this relationship disappeared when the effects of sleep and seizures were accounted for. Medication-specific analysis revealed that only levetiracetam showed a significant impact on spike rates during taper.&#xD;Significance. This study demonstrates that interictal spike rates are more strongly influenced by seizure activity and sleep than by ASM levels, suggesting limited utility of spike rates as a biomarker for ASM load. However, spikes may still serve as important markers for seizure control and disease severity.&#xD;Key points:&#xD;● Interictal spike rates do not track ASM levels consistently. &#xD;● Spikes are more indicative of seizure activity and sleep than of changing ASM levels.&#xD;● Spikes may still aid in assessing disease severity and epilepsy management, particularly in relation to seizure activity.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031532","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":"NaviNIBS: a comprehensive and open-source software toolbox for neuronavigated noninvasive brain stimulation.","authors":"Christopher C Cline, Lily Forman, Winn Hartford, Jade Truong, Sara Parmigiani, Corey J Keller","doi":"10.1088/1741-2552/adfab2","DOIUrl":"10.1088/1741-2552/adfab2","url":null,"abstract":"<p><p><i>Objective.</i>Image-guided positioning, or neuronavigation, is critical for precise targeting of transcranial magnetic stimulation (TMS) and other noninvasive brain stimulation. However, existing commercial systems have limitations in flexibility and extensibility for research applications.<i>Approach.</i>We present new open-source software for neuronavigated non-invasive brain stimulation (NaviNIBS) that provides comprehensive functionality for TMS experiments. NaviNIBS supports imaging data import, target planning, head registration, real-time tool tracking, and integration with robotic positioning and electrophysiology systems. Key features include flexible target specification, support for multiple tracking hardware options, refined head registration techniques, and an extensible addon system.<i>Main results.</i>We describe the software architecture, core functionality, characterization of tracking performance, and example applications of NaviNIBS.<i>Significance.</i>By implementing NaviNIBS and sharing it with the research community, we aim to facilitate methodological improvements and novel experimental paradigms in noninvasive brain stimulation research.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839438","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":"Reconstructing high-resolution visual perceptual images from human intracranial electrocorticography signals.","authors":"Yongjie Deng, Xiaolong Wu, Xin Gao, Weizhong Li, Dingguo Zhang","doi":"10.1088/1741-2552/ae0070","DOIUrl":"10.1088/1741-2552/ae0070","url":null,"abstract":"<p><p><i>Objectives.</i>Reconstruction of visual perception from brain signals has emerged as a promising research topic. Electrocorticography (ECoG) is a kind of high-quality intracranial signal with good spatiotemporal resolution that offers some new opportunities. However, according to our knowledge, there are no studies to reconstruct the perceived images from human ECoG signals at present.<i>Approach.</i>We have conducted the pioneering work and developed a novel pipeline that integrates Talairach coordinate alignment masked autoencoders (TA-MAE) with denoising diffusion probabilistic models. Our approach exploits the spatiotemporal dynamics of human ECoG signals, enabling the restoration of details in high-resolution.<i>Main results.</i>Experiments show that our method outperforms the current state-of-the-art methods in terms of appearance, structure, signal-noise ratio, and semantic consistency. Additionally, our study indicated that unsupervised learning-based signal reconstruction outperforms manually annotated label-guided feature recognition in capturing the low-dimensional representation of brain signals, potentially facilitating the exploration of vision's intrinsic mechanisms.<i>Significance.</i>These results highlight the advantages of unsupervised decoding and provide a generalizable framework for human ECoG-based visual reconstruction.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984091","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":"Unveiling functional heterogeneity in anatomical functional areas: a framework for fine-grained functional connectivity analysis of wide-field calcium imaging data.","authors":"Yunhan Li, Yiwen Xu, Yaqin Liu, Chang'an A Zhan","doi":"10.1088/1741-2552/adff39","DOIUrl":"10.1088/1741-2552/adff39","url":null,"abstract":"<p><p><i>Objective</i>. Conventional functional connectivity (FC) analysis of wide-field calcium imaging (WFCI) data relies on the assumption of homogeneity within predefined anatomical functional areas (FAs), where the signal averaged within each FA serves as the foundation for inter-FA connectivity modeling. However, accumulating evidence suggested significant intra-FA functional heterogeneity with functionally distinct subregions. This study aims to systematically examine the existence of the heterogeneity and the consequences of violating the homogeneity assumption on the FC analysis.<i>Approach</i>. We propose a three-step approach: (1) The spatiotemporal clustering of pixels with similar activity dynamics into functional parcels (FPs). (2) Classifying FPs according to their cortex-wide connectivity profiles into four categories: cross-hemispheric (CH), unihemispheric (UH), cross-modal (CM) and unimodal (UM). (3) Gauging the consequences and utility of above analysis.<i>Main results</i>. Analysis of adult mice WFCI data (<i>n</i>= 6) shows that pixels can be reliably clustered into FPs, and that FPs fall into different categories and form distinct subregions, unveiling the functional heterogeneity within each FA. Crucially, fine-grained FC analysis for different categories of subregions uncovered significant differences compared to the results from the conventional method. Application of the analysis to longitudinal WFCI data on mouse brain development (<i>n</i>= 17) demonstrates increases in CM and CH subregions and decreases in UM and UH subregions over time, in line with expectations based on prior research into neurodevelopment and network reorganization.<i>Significance</i>. The present study develops a fine-grained FC analysis framework, leveraging the high spatiotemporal resolution of WFCI to more precisely characterize large-scale cortical network dynamics. The observed differences between FCs derived using the proposed framework and those from conventional methods highlight the need for caution regarding the functional homogeneity assumption in conventional FC analysis. Furthermore, the developmental regularities revealed in longitudinal mouse brain data demonstrate the utility of fine-grained FC analysis.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984141","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":"Extracting robust single-trial somatosensory evoked potentials for non-invasive brain computer interfaces.","authors":"Disha Gupta, Jodi Brangaccio, Helia Mojtabavi, Jonathan Wolpaw, N Jeremy Hill","doi":"10.1088/1741-2552/adfd8a","DOIUrl":"10.1088/1741-2552/adfd8a","url":null,"abstract":"<p><p><i>Objective.</i>Reliable extraction of single-trial somatosensory evoked potentials (SEPs) is essential for developing brain-computer interface (BCI) applications to support rehabilitation after brain injury. For real-time feedback, these responses must be extracted prospectively on every trial, with minimal post-processing and artifact correction. However, noninvasive SEPs elicited by electrical stimulation at recommended parameter settings (0.1-0.2 msec pulse width, stimulation at or below motor threshold, 2-5 Hz frequency) are typically small and variable, often requiring averaging across multiple trials or extensive processing. Here, we describe and evaluate ways to optimize the stimulation setup to enhance the signal-to-noise ratio (SNR) of noninvasive single-trial SEPs, enabling more reliable extraction.<i>Approach.</i>SEPs were recorded with scalp electroencephalography in tibial nerve stimulation in thirteen healthy people, and two people with CNS injuries. Three stimulation frequencies (lower than recommended: 0.2 Hz, 1 Hz, 2 Hz) with a pulse width longer than recommended (1 msec), at a stimulation intensity based on H-reflex and M-wave at Soleus muscle were evaluated. Detectability of single-trial SEPs relative to background noise was tested offline and in a pseudo-online analysis, followed by a real-time demonstration.<i>Main</i><i>results.</i>SEP N70 was observed predominantly at the central scalp regions. Online decoding performance was significantly higher with Laplacian filter. Generalization performance showed an expected degradation, at all frequencies, with an average decrease of 5.9% (multivariate) and 6.5% (univariate), with an AUC score ranging from 0.78-0.90. The difference across stimulation frequencies was not significant. In individuals with injuries, AUC of 0.86 (incomplete spinal cord injury) and 0.81 (stroke) was feasible. Real-time demonstration showed SEP detection with AUC of 0.89.<i>Significance.</i>This study describes and evaluates a system for extracting single-trial SEPs in real-time, suitable for a BCI-based operant conditioning. It enhances SNR of individual SEPs by alternate electrical stimulation parameters, dry headset, and optimized signal processing.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12405709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984201","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":"Instantaneous recognition method for lower limb continuous motion based on onset-window surface electromyography data.","authors":"Xiaohui Li, Hao Zhou, Xueyan Lyu, Xiaoyue Yu, Dezhi Yu, Wenzhuo Wang, Guanglin Li, Lin Wang","doi":"10.1088/1741-2552/adfab3","DOIUrl":"10.1088/1741-2552/adfab3","url":null,"abstract":"<p><p><i>Objective</i>. Human-robot collaboration in lower-limb rehabilitation devices imposes stringent requirements on both the recognition accuracy of motion intention and real-time responsiveness. The precise recognition of lower limb motion based on surface electromyography (sEMG) has always been a primary focus of study. However, achieving low-delay recognition in lower limb continuous motion while maintaining accuracy remains a challenge, which is key to unlocking the full potential for the effective deployment and widespread application of robots.<i>Approach</i>. An innovative recognition method in lower limb continuous motion was presented in this paper, which investigated the instantaneous recognition network (IRN) and continuous recognition (CR) model.<i>Main results.</i>The comparative analysis revealed that by utilizing an optimal length of 210 for the onset-window sEMG data, the proposed IRN could substantially reduce the time delay from 300 ms/350 ms to 60 ms at the methodological level. The implementation of the class-balanced method enhanced motion recognition accuracy by an additional 4.83% within the onset window. The CR model was validated across seven scenarios, comprehensively covering all potential situations in daily continuous movements, and achieved an average accuracy of 96.31%.<i>Significance.</i>This study demonstrates the potential of the proposed instantaneous recognition method to enhance performance in lower limb continuous motion, providing an innovative approach for research on human-robot synchronization.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839437","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":"Refining signal localization: a transcranial magnetic stimulation motor mapping approach to functional near-infrared spectroscopy.","authors":"Garrett S Black, Joshua A Dugdale, Jordan A Borrell","doi":"10.1088/1741-2552/adfd8b","DOIUrl":"https://doi.org/10.1088/1741-2552/adfd8b","url":null,"abstract":"<p><p><i>Objective.</i>Functional near-infrared spectroscopy (fNIRS) has emerged as a promising brain imaging tool due to its cost-effectiveness and balance between spatial and temporal resolution. However, its reliance on the 10-20 EEG coordinate system for probe placement introduces potential inaccuracies in cortical localization. Despite concerns regarding its spatial precision, the integration of transcranial magnetic stimulation (TMS) with fNIRS for validating signal localization has not been systematically explored. This study aimed to demonstrate the interindividual variability in hand motor representations and how it influences the precision of fNIRS recordings during motor tasks.<i>Approach.</i>Neuronavigated TMS was employed on 18 neurotypical adults to map the motor representations of the first dorsal interosseous (FDI) and fourth dorsal interosseous (4DI) muscles. Center-of-gravity (CoG) coordinates from TMS-evoked motor maps were compared with fNIRS channel locations, including the theoretical hand channel defined by the 10-20 EEG system. FNIRS signals were recorded during a hand-grasp motor task, and the subject-specific hand channel was determined by identifying the fNIRS channel closest to the individual's TMS CoG.<i>Main Results.</i>TMS motor mapping revealed substantial interindividual variability, with 56% of participants demonstrating deviations from the theoretical fNIRS hand channel. TMS motor maps showed that the FDI and 4DI representations were closely positioned, with the 4DI representation slightly anterior to the FDI (<i>p</i>= 0.022). Analysis of fNIRS signals indicated that subject-specific hand channels exhibited significantly higher hemodynamic response amplitudes compared to the theoretical hand channel (<i>p</i>= 0.0004), suggesting enhanced signal sensitivity when using individualized cortical mapping. Additionally, fNIRS signal variance was significantly higher in the theoretical channel, indicating greater signal variability and lower signal robustness.<i>Significance.</i>These findings highlight the limitations of rigidly applying the 10-20 EEG system for spatial localization in fNIRS-based motor studies and show the benefits of integrating TMS-derived cortical mapping for improved signal accuracy and robustness.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984179","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 stimulation-free automatic electrocorticographic speech mapping in neurosurgery patients.","authors":"Alexei Voskoboynikov, Magomed Aliverdiev, Yulia Nekrasova, Ilia Semenkov, Anastasia Skalnaya, Mikhail Sinkin, Alexei Ossadtchi","doi":"10.1088/1741-2552/adfc9c","DOIUrl":"10.1088/1741-2552/adfc9c","url":null,"abstract":"<p><p><i>Objective.</i>The precise mapping of speech-related functions is crucial for successful neurosurgical interventions in epilepsy and brain tumor cases. Traditional methods like electrocortical stimulation mapping (ESM) are effective but carry a significant risk of inducing seizures.<i>Methods.</i>To address this, we have prepared a comprehensive ESM + electrocorticographic mapping (ECM) dataset from 14 patients with chronically implanted stereo-EEG electrodes. Then we explored several compact machine learning (ML) approaches to convert the ECM signals to the ground truth derived from the risky ESM procedure. Both procedures involved the standard picture naming task. As features, we used gamma-band power within successive temporal windows in the data averaged with respect to picture and voice onsets. We focused on a range of classifiers, including XGBoost, linear support vector classification (SVC), regularized logistic regression, random forest,<i>k</i>-nearest neighbors, decision tree, multi-Layer perceptron, AdaBoost and Gaussian Naive Bayes classifiers and equipped them with confidence interval estimates, crucial in a real-life application. We validated the ML approaches using a leave-one-patient-out procedure and computed ROC and Precision-Recall curves for various feature combinations.<i>Results.</i>For linear SVC we achieved ROC-AUC and PR-AUC scores of 0.91 and 0.88, respectively, which effectively distinguishes speech-related from non-related iEEG channels. We have also observed that the use of information on the voice onset moment notably improved the classification accuracy.<i>Significance.</i>We have for the first time rigorously compared the ECM and ESM results and mimicked a real-life use of the ECM technology. We have also provided public access to the comprehensive ECM+ESM dataset to pave the road towards safer and more reliable eloquent cortex mapping procedures.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877647","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":"Deep brain stimulation-induced local evoked potentials outperform spectral features in spatial and clinical STN mapping.","authors":"Enrico Opri, Faical Isbaine, Seyyed Bahram Borgheai, Emily Bence, Roohollah Jafari Deligani, Jon T Willie, Robert E Gross, Nicholas Au Yong, Svjetlana Miocinovic","doi":"10.1088/1741-2552/adf99f","DOIUrl":"10.1088/1741-2552/adf99f","url":null,"abstract":"<p><p><i>Objective.</i>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established therapy for Parkinson's disease (PD). Yet, optimizing lead placement and stimulation programming remains challenging. Current techniques rely on imaging and intraoperative microelectrode recordings (MER), while programming relies on trial-and-error clinical testing, which can be time-consuming. DBS-induced local evoked potentials (DLEP), also known as evoked resonant neural activity, have emerged as a potential alternative electrophysiological marker for mapping. However, direct comparisons with traditional spectral features, such as beta-band, high-frequency oscillations (HFOs), and aperiodic component are lacking.<i>Approach.</i>We evaluated DLEP across 39 STN DBS leads in 31 subjects with PD undergoing DBS surgery, using both a single-pulse and high-frequency (HF) burst stimulation paradigms. We developed a novel artifact-removal method to enable monopolar DLEP recovery, including estimating the DLEP amplitudes at stimulated contacts, further enhancing spatial sampling of DLEP. We evaluated spectral features and DLEP in respect to imaging-based and MER-based localization, and its predictive power for post-operative programming.<i>Main results.</i>DLEP showed great spatial consistency, maximizing within STN with 100% accuracy for single-pulse and 84.62% for burst stimulation, surpassing spectral measures including beta (89.74%) and HFO (82.05%). DLEP better correlated with clinical outcomes (single-pulses<i>ρ</i>= -0.33, HF bursts<i>ρ</i>= -0.26), than spectral measures (beta<i>ρ</i>= -0.25, HFO<i>ρ</i>= 0.05). Furthermore, single-pulses at low-frequencies are sufficient for DLEP-based mapping.<i>Significance.</i>We show how DLEP provide higher STN-spatial specificity and correlation with postoperative programming compared to spectral features. To support clinical translation of DLEP, we developed two methods aimed to recover artifact-free DLEP and estimating DLEP amplitudes at stimulating contacts. DLEP appear distinct from beta and HFO activity, yet strongly tied to aperiodic spectral components, suggesting that DLEP amplitude reflects underlying STN excitability. This study highlights that DLEP are a robust and clinically valuable marker for DBS targeting and programming.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805526","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}