Journal of neural engineering最新文献

{"title":"Improving pre-movement patterns detection with multi-dimensional EEG features for readiness potential decrease.","authors":"Lipeng Zhang, Hongyu Zhang, Shaoting Yan, Ruiqi Li, Dezhong Yao, Yuxia Hu, Rui Zhang","doi":"10.1088/1741-2552/adaef2","DOIUrl":"10.1088/1741-2552/adaef2","url":null,"abstract":"<p><p><i>Objective.</i>The readiness potential (RP) is an important neural characteristic in motor preparation-based brain-computer interface. In our previous research, we observed a significant decrease of the RP amplitude in some cases, which severely affects the pre-movement patterns detection. In this paper, we aimed to improve the accuracy (Acc) of pre-movement patterns detection in the condition of RP decrease.<i>Approach.</i>We analyzed multi-dimensional EEG features in terms of time-frequency, brain networks, and cross-frequency coupling (CFC). And, a multi-dimensional Electroencephalogram feature combination (MEFC) algorithm was proposed. The features used include: (1) waveforms of the RP; (2) energy in alpha and beta bands; (3) brain network in alpha and beta bands; and (4) CFC value between 2 and 10 Hz.<i>Main results.</i>By employing support vector machines, the MEFC method achieved an average recognition rate of 88.9% and 85.5% under normal and RP decrease conditions, respectively. Compared to classical algorithm, the average Acc for both tasks improved by 7.8% and 8.8% respectively.<i>Significance.</i>This method can effectively improve the Acc of pre-movement patterns decoding in the condition of RP decrease.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054698","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":"EEG correlates of acquiring race driving skills.","authors":"M Sultana, L Gheorghe, S Perdikis","doi":"10.1088/1741-2552/adb077","DOIUrl":"10.1088/1741-2552/adb077","url":null,"abstract":"<p><p><i>Objective</i>. Race driving is a complex motor task that involves multiple concurrent cognitive processes in different brain regions coordinated to maintain and optimize speed and control. Delineating the neuroplasticity accompanying the acquisition of complex and fine motor skills such as racing is crucial to elucidate how these are gradually encoded in the brain and inform new training regimes. This study aims, first, to identify the neural correlates of learning to drive a racing car using non-invasive electroencephalography (EEG) imaging and longitudinal monitoring. Second, we gather evidence on the potential role of transcranial direct current stimulation (tDCS) in enhancing the training outcome of race drivers.<i>Approach</i>. We collected and analyzed multimodal experimental data, including drivers' EEG and telemetry from a driving simulator to identify neuromarkers of race driving proficiency and assess the potential to improve training through anodal tDCS.<i>Main results</i>. Our findings indicate that theta-band EEG rhythms and alpha-band effective functional connectivity between frontocentral and occipital cortical areas are significant neuromarkers for acquiring racing skills. We also observed signs of a potential tDCS effect in accelerating the learning process.<i>Significance</i>These results provide a foundation for future research to develop innovative race-driving training protocols using neurotechnology.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070456","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":"How different immersive environments affect intracortical brain computer interfaces.","authors":"Ariana F Tortolani, Nicolas G Kunigk, Anton R Sobinov, Michael L Boninger, Sliman J Bensmaia, Jennifer L Collinger, Nicholas G Hatsopoulos, John E Downey","doi":"10.1088/1741-2552/adb078","DOIUrl":"10.1088/1741-2552/adb078","url":null,"abstract":"<p><p><i>Objective</i>. As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device.<i>Approach</i>. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor.<i>Main results</i>. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered.<i>Significance</i>. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.Clinical Trial: NCT01894802.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11809280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070462","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":"ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.","authors":"Yuxin Qin, Baojiang Li, Wenlong Wang, Xingbin Shi, Cheng Peng, Xichao Wang, Haiyan Wang","doi":"10.1088/1741-2552/adaf58","DOIUrl":"10.1088/1741-2552/adaf58","url":null,"abstract":"<p><p><i>Objective</i>. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain. However, the performance of MI-based unimodal classification methods is low due to the limitations of EEG or fNIRS.<i>Approach</i>. In this paper, we propose a new multimodal fusion classification method capable of combining the potential complementary advantages of EEG and fNIRS. First, we propose a feature extraction network capable of extracting spatio-temporal features from EEG-based and fNIRS-based MI signals. Then, we successively fused the EEG and fNIRS at the feature-level and the decision-level to improve the adaptability and robustness of the model.<i>Main results</i>. We validate the performance of ECA-FusionNet on a publicly available EEG-fNIRS dataset. The results show that ECA-FusionNet outperforms unimodal classification methods, as well as existing fusion classification methods, in terms of classification accuracy for MI.<i>Significance</i>. ECA-FusionNet may provide a useful reference for the field of multimodal fusion classification.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061861","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 low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.","authors":"Benjamin J Choi, Ji Liu","doi":"10.1088/1741-2552/adae35","DOIUrl":"10.1088/1741-2552/adae35","url":null,"abstract":"<p><p><i>Objective.</i>Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures.<i>Approach.</i>To address the shortcomings of current noninvasive neural monitoring techniques-namely, single-channel EEG-we leveraged machine learning (ML), creating a neural network-based EEG interpretation algorithm. ML generation was guided by two underlying goals: (1) to improve overall system performance by combining discrete models using a prediction voting scheme, and (2) to favor model<i>diversity</i>within these new neural network ensembles, as opposed to individual model<i>performance</i>. EEG data from eight frequency bands was collected from human subjects to train a ML algorithm employing a hierarchical mixture-of-experts structure. We also implemented head gesture-based control to assist in the generation of additional stable classes for the control system.<i>Main results.</i>The final model performs competitively with existing EEG interpretation systems. Inertial measurement unit (IMU)-based head gestures supplement the neural control system, with 270° actuation of synovial elbow and radial wrist joints driven by intuitive corresponding head gestures. The brain-controlled prosthesis presented in this study costs US$300 to manufacture and achieved competitive performance on a Box and Block Test.<i>Significance.</i>These results suggest proof-of-concept for potential application as an alternative to current prosthetics, but it is important to note that the demonstration in this study remains exploratory. Future work includes broader clinical testing and exploring further uses for the developed ML system.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043816","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":"Pitfalls and practical suggestions for using local field potential recordings in DBS clinical practice and research.","authors":"Bart E K S Swinnen, Arthur W G Buijink, Mariëlle J Stam, Deborah Hubers, Martijn de Neeling, Bart J Keulen, Francesca Morgante, Bernadette C M van Wijk, Rob M A de Bie, Lucia Ricciardi, Simon J Little, Martijn Beudel","doi":"10.1088/1741-2552/adaeee","DOIUrl":"10.1088/1741-2552/adaeee","url":null,"abstract":"<p><p><i>Objective</i>. Local field potential (LFP) recordings using chronically implanted sensing-enabled stimulators are a powerful tool for indexing symptom presence and severity in neurological and neuropsychiatric disorders, and for enhancing our neurophysiological understanding of brain processes. LFPs have gained interest as input signals for closed-loop deep brain stimulation (DBS) and can be used to inform DBS parameter selection. LFP recordings using chronically implanted sensing-enabled stimulators have various implementational challenges.<i>Approach</i>. Here we describe our collective experience using BrainSense (Medtronic®) for clinical and research work. We aim to provide insightful tips and practical advice to empower readers with the knowledge needed to navigate the intricacies of the device and make the most out of its features.<i>Main results</i>. The central issues that apply to several BrainSense features encompass restricted compatibility of stimulation configuration with sensing, differences in electrophysiological signal properties between 'stimulation OFF' and 'stimulation ON at 0.0 mA', and challenges associated with the internal clock of the neurostimulator. In addition, since recordings are obtained from bipolar and not monopolar channels, spatial certainty regarding the distribution of LFPs around the DBS electrode is limited. Several options exist to synchronize LFP time series with external data streams, but standardization and generalization are lacking. The use of at-home chronic LFP recording is limited by a low temporal and spectral resolution. Regarding at-home LFP snapshots, LFP time series are not stored, parts of the power spectrum are censored when stimulating at high or low frequencies, and the stimulation amplitude is not readily available.<i>Significance</i>. We discussed practical applications, implementation, system limitations, and pitfalls with the aim that sensing can be better applied for clinical practice and research.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054702","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":"The 'Brussels 4': essential requirements for implantable brain-computer interface user autonomy.","authors":"Thomas J Oxley, Darrel R Deo, Stephanie Cernera, Abbey Sawyer, David Putrino, Nick F Ramsey, Adam Fry","doi":"10.1088/1741-2552/ada0e6","DOIUrl":"10.1088/1741-2552/ada0e6","url":null,"abstract":"<p><p><i>Objective</i>. Implantable brain-computer interfaces (iBCIs) hold great promise for individuals with severe paralysis and are advancing toward commercialization. The features required for successful clinical translation and patient adoption of iBCIs may be under recognized within traditional academic iBCI research and deserve further consideration.<i>Approach</i>. Here we consider potentially critical factors to achieve iBCI user autonomy, reflecting the authors' perspectives on discussions during various sessions and workshops across the 10th International BCI Society Meeting, Brussels, 2023.<i>Main results</i>. Four key considerations were identified: (1) immediate use, (2) easy to use, (3) continuous use, and (4) stable system use.<i>Significance</i>. Addressing these considerations may enable successful clinical translation of iBCIs.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857446","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":"Plug-and-play myoelectric control via a self-calibrating random forest common model.","authors":"Xinyu Jiang, Chenfei Ma, Kianoush Nazarpour","doi":"10.1088/1741-2552/adada0","DOIUrl":"10.1088/1741-2552/adada0","url":null,"abstract":"<p><p><i>Objective</i>. Electromyographic (EMG) signals show large variabilities over time due to factors such as electrode shifting, user behavior variations, etc substantially degrading the performance of myoelectric control models in long-term use. Previously one-time model calibration was usually required each time before usage. However, the EMG characteristics could change even within a short period of time. Our objective is to develop a self-calibrating model, with an automatic and unsupervised self-calibration mechanism.<i>Approach</i>. We developed a computationally efficient random forest (RF) common model, which can (1) be pre-trained and easily adapt to a new user via one-shot calibration, and (2) keep calibrating itself once in a while by boosting the RF with new decision trees trained on pseudo-labels of testing samples in a data buffer.<i>Main results</i>. Our model has been validated in both offline and real-time, both open and closed-loop, both intra-day and long-term (up to 5 weeks) experiments. We tested this approach with data from 66 non-disabled participants. We also explored the effects of bidirectional user-model co-adaption in closed-loop experiments. We found that the self-calibrating model could gradually improve its performance in long-term use. With visual feedback, users will also adapt to the dynamic model meanwhile learn to perform hand gestures with significantly lower EMG amplitudes (less muscle effort).<i>Significance</i>. Our RF-approach provides a new alternative built on simple decision tree for myoelectric control, which is explainable, computationally efficient, and requires minimal data for model calibration. Source codes are avaiable at:https://github.com/MoveR-Digital-Health-and-Care-Hub/self-calibrating-rf.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030579","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":"Frequency-dependent phase entrainment of cortical cell types during tACS: computational modeling evidence.","authors":"Gabriel Gaugain, Mariam Al Harrach, Maxime Yochum, Fabrice Wendling, Marom Bikson, Julien Modolo, Denys Nikolayev","doi":"10.1088/1741-2552/ad9526","DOIUrl":"10.1088/1741-2552/ad9526","url":null,"abstract":"<p><p><i>Objective</i>. Transcranial alternating current stimulation (tACS) enables non-invasive modulation of brain activity, holding promise for clinical and research applications. Yet, it remains unclear how the stimulation frequency differentially impacts various neuron types. Here, we aimed to quantify the frequency-dependent behavior of key neocortical cell types.<i>Approach</i>. We used both detailed (anatomical multicompartments) and simplified (three compartments) single-cell modeling approaches based on the Hodgkin-Huxley formalism to study neocortical excitatory and inhibitory cells under various tACS intensities and frequencies within the 5-50 Hz range at rest and during basal 10 Hz activity.<i>Main results</i>. L5 pyramidal cells (PCs) exhibited the highest polarizability at direct current, ranging from 0.21 to 0.25 mm and decaying exponentially with frequency. Inhibitory neurons displayed membrane resonance in the 5-15 Hz range with lower polarizability, although bipolar cells had higher polarizability. Layer 5 PC demonstrated the highest entrainment close to 10 Hz, which decayed with frequency. In contrast, inhibitory neurons entrainment increased with frequency, reaching levels akin to PC. Results from simplified models could replicate phase preferences, while amplitudes tended to follow opposite trends in PC.<i>Significance</i>. tACS-induced membrane polarization is frequency-dependent, revealing observable resonance behavior. Whilst optimal phase entrainment of sustained activity is achieved in PC when tACS frequency matches endogenous activity, inhibitory neurons tend to be entrained at higher frequencies. Consequently, our results highlight the potential for precise, cell-specific targeting for tACS.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684060","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 multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.","authors":"Qikai Fan, Lurong Jiang, Amira El Gohary, Fang Dong, Duanpo Wu, Tiejia Jiang, Chen Wang, Junbiao Liu","doi":"10.1088/1741-2552/adaef3","DOIUrl":"10.1088/1741-2552/adaef3","url":null,"abstract":"<p><p><i>Objective.</i>The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper proposes a seizure detection method with spike-based phase locking value (PLV) functional brain networks and multi-domain fused features.<i>Approach.</i>In the spiking detection part, brain functional networks based on PLV are constructed to explore the changes in brain functional states during spiking discharge, from the perspective of microscopic neuronal activity to macroscopic brain region interactions. Then, in the epilepsy seizure detection task, multi-domain fused feature sequences are constructed using time-domain, frequency-domain, inter-channel correlation, and the spike detection features. Finally, Bi-LSTM and Transformer encoders and their optimized models are used to verify the effectiveness of the proposed method.<i>Main results.</i>Experimental results achieve the best seizure detection metrics on Bi-LSTM-Attention, with accuracy, sensitivity, and specificity reaching 98.40%, 98.94%, and 97.86%, respectively.<i>Significance.</i>The method is significant as it innovatively applies multi channel spike network features to seizure detection. It can potentially improve the diagnosis and location of the epileptogenic region by accurately detecting seizures through the identification of spikes, which is a crucial characteristic wave of epilepsy.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054694","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}