Journal of neural engineering最新文献

{"title":"MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition","authors":"Guangqiang Li, Ning Chen, Yixiang Niu, Zhangyong Xu, Yuxuan Dong, Jing Jin, Hongqin Zhu","doi":"10.1088/1741-2552/ad3c28","DOIUrl":"https://doi.org/10.1088/1741-2552/ad3c28","url":null,"abstract":"<italic toggle=\"yes\">Objective</italic>. The instability of the EEG acquisition devices may lead to information loss in the channels or frequency bands of the collected EEG. This phenomenon may be ignored in available models, which leads to the overfitting and low generalization of the model. <italic toggle=\"yes\">Approach</italic>. Multiple self-supervised learning tasks are introduced in the proposed model to enhance the generalization of EEG emotion recognition and reduce the overfitting problem to some extent. Firstly, channel masking and frequency masking are introduced to simulate the information loss in certain channels and frequency bands resulting from the instability of EEG, and two self-supervised learning-based feature reconstruction tasks combining masked graph autoencoders (GAE) are constructed to enhance the generalization of the shared encoder. Secondly, to take full advantage of the complementary information contained in these two self-supervised learning tasks to ensure the reliability of feature reconstruction, a weight sharing (WS) mechanism is introduced between the two graph decoders. Thirdly, an adaptive weight multi-task loss (AWML) strategy based on homoscedastic uncertainty is adopted to combine the supervised learning loss and the two self-supervised learning losses to enhance the performance further. <italic toggle=\"yes\">Main results</italic>. Experimental results on SEED, SEED-V, and DEAP datasets demonstrate that: (i) Generally, the proposed model achieves higher averaged emotion classification accuracy than various baselines included in both subject-dependent and subject-independent scenarios. (ii) Each key module contributes to the performance enhancement of the proposed model. (iii) It achieves higher training efficiency, and significantly lower model size and computational complexity than the state-of-the-art (SOTA) multi-task-based model. (iv) The performances of the proposed model are less influenced by the key parameters. <italic toggle=\"yes\">Significance</italic>. The introduction of the self-supervised learning task helps to enhance the generalization of the EEG emotion recognition model and eliminate overfitting to some extent, which can be modified to be applied in other EEG-based classification tasks.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"48 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peripheral direct current reduces naturally evoked nociceptive activity at the spinal cord in rodent models of pain","authors":"Tom F Su, Jack D Hamilton, Yiru Guo, Jason R Potas, Mohit N Shivdasani, Gila Moalem-Taylor, Gene Y Fridman, Felix P Aplin","doi":"10.1088/1741-2552/ad3b6c","DOIUrl":"https://doi.org/10.1088/1741-2552/ad3b6c","url":null,"abstract":"<italic toggle=\"yes\">Objective.</italic> Electrical neuromodulation is an established non-pharmacological treatment for chronic pain. However, existing devices using pulsatile stimulation typically inhibit pain pathways indirectly and are not suitable for all types of chronic pain. Direct current (DC) stimulation is a recently developed technology which affects small-diameter fibres more strongly than pulsatile stimulation. Since nociceptors are predominantly small-diameter A<italic toggle=\"yes\">δ</italic> and C fibres, we investigated if this property could be applied to preferentially reduce nociceptive signalling. <italic toggle=\"yes\">Approach.</italic> We applied a DC waveform to the sciatic nerve in rats of both sexes and recorded multi-unit spinal activity evoked at the hindpaw using various natural stimuli corresponding to different sensory modalities rather than broad-spectrum electrical stimulus. To determine if DC neuromodulation is effective across different types of chronic pain, tests were performed in models of neuropathic and inflammatory pain. <italic toggle=\"yes\">Main results.</italic> We found that in both pain models tested, DC application reduced responses evoked by noxious stimuli, as well as tactile-evoked responses which we suggest may be involved in allodynia. Different spinal activity of different modalities were reduced in naïve animals compared to the pain models, indicating that physiological changes such as those mediated by disease states could play a larger role than previously thought in determining neuromodulation outcomes. <italic toggle=\"yes\">Significance.</italic> Our findings support the continued development of DC neuromodulation as a method for reduction of nociceptive signalling, and suggests that it may be effective at treating a broader range of aberrant pain conditions than existing devices.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"33 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards ASSR-based hearing assessment using natural sounds","authors":"Anna Sergeeva, Christian Bech Christensen, Preben Kidmose","doi":"10.1088/1741-2552/ad3b6b","DOIUrl":"https://doi.org/10.1088/1741-2552/ad3b6b","url":null,"abstract":"<italic toggle=\"yes\">Objective</italic>. The auditory steady-state response (ASSR) allows estimation of hearing thresholds. The ASSR can be estimated from electroencephalography (EEG) recordings from electrodes positioned on both the scalp and within the ear (ear-EEG). Ear-EEG can potentially be integrated into hearing aids, which would enable automatic fitting of the hearing device in daily life. The conventional stimuli for ASSR-based hearing assessment, such as pure tones and chirps, are monotonous and tiresome, making them inconvenient for repeated use in everyday situations. In this study we investigate the use of natural speech sounds for ASSR estimation. <italic toggle=\"yes\">Approach.</italic> EEG was recorded from 22 normal hearing subjects from both scalp and ear electrodes. Subjects were stimulated monaurally with 180 min of speech stimulus modified by applying a 40 Hz amplitude modulation (AM) to an octave frequency sub-band centered at 1 kHz. Each 50 ms sub-interval in the AM sub-band was scaled to match one of 10 pre-defined levels (0–45 dB sensation level, 5 dB steps). The apparent latency for the ASSR was estimated as the maximum average cross-correlation between the envelope of the AM sub-band and the recorded EEG and was used to align the EEG signal with the audio signal. The EEG was then split up into sub-epochs of 50 ms length and sorted according to the stimulation level. ASSR was estimated for each level for both scalp- and ear-EEG. <italic toggle=\"yes\">Main results</italic>. Significant ASSRs with increasing amplitude as a function of presentation level were recorded from both scalp and ear electrode configurations. <italic toggle=\"yes\">Significance</italic>. Utilizing natural sounds in ASSR estimation offers the potential for electrophysiological hearing assessment that are more comfortable and less fatiguing compared to existing ASSR methods. Combined with ear-EEG, this approach may allow convenient hearing threshold estimation in everyday life, utilizing ambient sounds. Additionally, it may facilitate both initial fitting and subsequent adjustments of hearing aids outside of clinical settings.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"13 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-modal credibility modelling for EEG-based multimodal emotion recognition","authors":"Yuzhe Zhang, Huan Liu, Di Wang, Dalin Zhang, Tianyu Lou, Qinghua Zheng, Chai Quek","doi":"10.1088/1741-2552/ad3987","DOIUrl":"https://doi.org/10.1088/1741-2552/ad3987","url":null,"abstract":"<italic toggle=\"yes\">Objective.</italic> The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals. <italic toggle=\"yes\">Approach.</italic> In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility. <italic toggle=\"yes\">Main results.</italic> We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments. <italic toggle=\"yes\">Significance.</italic> All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"221 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method","authors":"Zhengyuan Lv, Jingming Li, Li Yao, Xiaojuan Guo","doi":"10.1088/1741-2552/ad39a6","DOIUrl":"https://doi.org/10.1088/1741-2552/ad39a6","url":null,"abstract":"<italic toggle=\"yes\">Objective.</italic> Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation, overlooking the dynamic nature of the diffusion process and providing an incomplete representation of the predicted FC. <italic toggle=\"yes\">Approach.</italic> In this study, we propose an alternative approach based on the HDM. First, we introduced a multiple-timescale fusion method to capture the dynamic features of the diffusion process. Additionally, to enhance the smoothness of the predicted FC values, we employed the Wavelet reconstruction method to maintain local consistency and remove noise. Moreover, to provide a more accurate representation of the relationship between SC and FC, we calculated the linear transformation between the smoothed FC and the empirical FC. <italic toggle=\"yes\">Main results.</italic> We conducted extensive experiments in two independent datasets. By fusing different time scales in the diffusion process for predicting FC, the proposed method demonstrated higher predictive correlation compared with method considering only critical time points (Singlescale). Furthermore, compared with other existing methods, the proposed method achieved the highest predictive correlations of 0.6939 <inline-formula>\u0000<tex-math><?CDATA $ pm $?></tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:math>\u0000<inline-graphic xlink:href=\"jnead39a6ieqn1.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula> 0.0079 and 0.7302 <inline-formula>\u0000<tex-math><?CDATA $ pm $?></tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:math>\u0000<inline-graphic xlink:href=\"jnead39a6ieqn2.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula> 0.0117 on the two datasets respectively. We observed that the visual network at the network level and the parietal lobe at the lobe level exhibited the highest predictive correlations, indicating that the functional activity in these regions may be closely related to the direct diffusion of information between brain regions. <italic toggle=\"yes\">Significance.</italic> The multiple-timescale fusion method proposed in this study provides insights into the dynamic aspects of the diffusion process, contributing to a deeper understanding of how brain structure gives rise to brain function.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"60 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition","authors":"Wenjie Li, Haoyu Li, Xinlin Sun, Huicong Kang, Shan An, Guoxin Wang, Zhongke Gao","doi":"10.1088/1741-2552/ad3986","DOIUrl":"https://doi.org/10.1088/1741-2552/ad3986","url":null,"abstract":"<italic toggle=\"yes\">Objective</italic>. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals. <italic toggle=\"yes\">Approach</italic>. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network’s ability to extract deep features from original signals without relying on the true labels of the data. <italic toggle=\"yes\">Main results</italic>. To evaluate our framework’s efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32<inline-formula>\u0000<tex-math><?CDATA $%$?></tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mrow><mml:mi mathvariant=\"normal\">%</mml:mi></mml:mrow></mml:math>\u0000<inline-graphic xlink:href=\"jnead3986ieqn1.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula>, 82.34<inline-formula>\u0000<tex-math><?CDATA $%$?></tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mrow><mml:mi mathvariant=\"normal\">%</mml:mi></mml:mrow></mml:math>\u0000<inline-graphic xlink:href=\"jnead3986ieqn2.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula>, and 81.13<inline-formula>\u0000<tex-math><?CDATA $%$?></tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mrow><mml:mi mathvariant=\"normal\">%</mml:mi></mml:mrow></mml:math>\u0000<inline-graphic xlink:href=\"jnead3986ieqn3.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula> on the three datasets, demonstrating superior performance compared to existing methods. <italic toggle=\"yes\">Significance</italic>. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"108 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transferable non-invasive modal fusion-transformer (NIMFT) for end-to-end hand gesture recognition","authors":"Tianxiang Xu, Kunkun Zhao, Yuxiang Hu, Liang Li, Wei Wang, Fulin Wang, Yuxuan Zhou, Jianqing Li","doi":"10.1088/1741-2552/ad39a5","DOIUrl":"https://doi.org/10.1088/1741-2552/ad39a5","url":null,"abstract":"<italic toggle=\"yes\">Objective.</italic> Recent studies have shown that integrating inertial measurement unit (IMU) signals with surface electromyographic (sEMG) can greatly improve hand gesture recognition (HGR) performance in applications such as prosthetic control and rehabilitation training. However, current deep learning models for multimodal HGR encounter difficulties in invasive modal fusion, complex feature extraction from heterogeneous signals, and limited inter-subject model generalization. To address these challenges, this study aims to develop an end-to-end and inter-subject transferable model that utilizes non-invasively fused sEMG and acceleration (ACC) data. <italic toggle=\"yes\">Approach.</italic> The proposed non-invasive modal fusion-transformer (NIMFT) model utilizes 1D-convolutional neural networks-based patch embedding for local information extraction and employs a multi-head cross-attention (MCA) mechanism to non-invasively integrate sEMG and ACC signals, stabilizing the variability induced by sEMG. The proposed architecture undergoes detailed ablation studies after hyperparameter tuning. Transfer learning is employed by fine-tuning a pre-trained model on new subject and a comparative analysis is performed between the fine-tuning and subject-specific model. Additionally, the performance of NIMFT is compared to state-of-the-art fusion models. <italic toggle=\"yes\">Main results.</italic> The NIMFT model achieved recognition accuracies of 93.91%, 91.02%, and 95.56% on the three action sets in the Ninapro DB2 dataset. The proposed embedding method and MCA outperformed the traditional invasive modal fusion transformer by 2.01% (embedding) and 1.23% (fusion), respectively. In comparison to subject-specific models, the fine-tuning model exhibited the highest average accuracy improvement of 2.26%, achieving a final accuracy of 96.13%. Moreover, the NIMFT model demonstrated superiority in terms of accuracy, recall, precision, and F1-score compared to the latest modal fusion models with similar model scale. <italic toggle=\"yes\">Significance.</italic> The NIMFT is a novel end-to-end HGR model, utilizes a non-invasive MCA mechanism to integrate long-range intermodal information effectively. Compared to recent modal fusion models, it demonstrates superior performance in inter-subject experiments and offers higher training efficiency and accuracy levels through transfer learning than subject-specific approaches.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"108 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orthogonal extended infomax algorithm","authors":"Nicole Ille","doi":"10.1088/1741-2552/ad38db","DOIUrl":"https://doi.org/10.1088/1741-2552/ad38db","url":null,"abstract":"<italic toggle=\"yes\">Objective.</italic> The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster. <italic toggle=\"yes\">Approach.</italic> Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods. <italic toggle=\"yes\">Main results.</italic> OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard. <italic toggle=\"yes\">Significance.</italic> OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"39 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparation of PLCL/ECM nerve conduits by electrostatic spinning technique and evaluation in vitro and in vivo","authors":"Yizhan Ma, Runze Zhang, Xiaoyan Mao, Xiaoming Li, Ting Li, Fang Liang, Jing He, Lili Wen, Weizuo Wang, Xiao Li, Yanhui Zhang, Honghao Yu, Binhan Lu, Tianhao Yu, Qiang Ao","doi":"10.1088/1741-2552/ad3851","DOIUrl":"https://doi.org/10.1088/1741-2552/ad3851","url":null,"abstract":"<italic toggle=\"yes\">Objective</italic>. Artificial nerve scaffolds composed of polymers have attracted great attention as an alternative for autologous nerve grafts recently. Due to their poor bioactivity, satisfactory nerve repair could not be achieved. To solve this problem, we introduced extracellular matrix (ECM) to optimize the materials. <italic toggle=\"yes\">Approach.</italic> In this study, the ECM extracted from porcine nerves was mixed with Poly(L-Lactide-co-<italic toggle=\"yes\">ϵ</italic>-caprolactone) (PLCL), and the innovative PLCL/ECM nerve repair conduits were prepared by electrostatic spinning technology. The novel conduits were characterized by scanning electron microscopy (SEM), tensile properties, and suture retention strength test for micromorphology and mechanical strength. The biosafety and biocompatibility of PLCL/ECM nerve conduits were evaluated by cytotoxicity assay with Mouse fibroblast cells and cell adhesion assay with RSC 96 cells, and the effects of PLCL/ECM nerve conduits on the gene expression in Schwann cells was analyzed by real-time polymerase chain reaction (RT-PCR). Moreover, a 10 mm rat (Male Wistar rat) sciatic defect was bridged with a PLCL/ECM nerve conduit, and nerve regeneration was evaluated by walking track, mid-shank circumference, electrophysiology, and histomorphology analyses. <italic toggle=\"yes\">Main results.</italic> The results showed that PLCL/ECM conduits have similar microstructure and mechanical strength compared with PLCL conduits. The cytotoxicity assay demonstrates better biosafety and biocompatibility of PLCL/ECM nerve conduits. And the cell adhesion assay further verifies that the addition of ECM is more beneficial to cell adhesion and proliferation. RT-PCR showed that the PLCL/ECM nerve conduit was more favorable to the gene expression of functional proteins of Schwann cells. The <italic toggle=\"yes\">in vivo</italic> results indicated that PLCL/ECM nerve conduits possess excellent biocompatibility and exhibit a superior capacity to promote peripheral nerve repair. <italic toggle=\"yes\">Significance.</italic> The addition of ECM significantly improved the biocompatibility and bioactivity of PLCL, while the PLCL/ECM nerve conduit gained the appropriate mechanical strength from PLCL, which has great potential for clinical repair of peripheral nerve injuries.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"40 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140575769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)","authors":"Tao Xie, T. Foutz, M. Adamek, James R Swift, Cory S Inman, Joseph R Manns, E. Leuthardt, Jon T. Willie, Peter Brunner","doi":"10.1088/1741-2552/ad1385","DOIUrl":"https://doi.org/10.1088/1741-2552/ad1385","url":null,"abstract":"\u0000 Objective: Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals. Approach: To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from 9 human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in 9 human subjects. Results: MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5 to 10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with 94±1.47% sensitivity and 99±1.01% specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation. Significance: MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"21 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}