Pub Date : 2024-08-05DOI: 10.1088/1741-2552/ad618c
Yu Tung Lo, Lei Jiang, Ben Woodington, Sagnik Middya, Marcel Braendlein, Jordan Lewis William Lam, Mervyn Jun Rui Lim, Vincent Yew Poh Ng, Jai Prashanth Rao, Derrick Wei Shih Chan, Beng Ti Ang
Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.
{"title":"Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation.","authors":"Yu Tung Lo, Lei Jiang, Ben Woodington, Sagnik Middya, Marcel Braendlein, Jordan Lewis William Lam, Mervyn Jun Rui Lim, Vincent Yew Poh Ng, Jai Prashanth Rao, Derrick Wei Shih Chan, Beng Ti Ang","doi":"10.1088/1741-2552/ad618c","DOIUrl":"10.1088/1741-2552/ad618c","url":null,"abstract":"<p><p><i>Objective.</i>Micro-electrocorticographic (<i>μ</i>ECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to use<i>μ</i>ECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.<i>Approach. μ</i>ECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.<i>Main results.</i>Single-unit spikes could be extracted during chronic recording from<i>μ</i>ECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.<i>Significance. μ</i>ECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581934","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}
Pub Date : 2024-08-02DOI: 10.1088/1741-2552/ad658f
Janita Nissi, Otto Kangasmaa, Juhani Kataja, Nicolas Bouisset, Ilkka Laakso
Objective. Normal function of the vestibular system can be disturbed using a noninvasive technique called electrical vestibular stimulation (EVS), which alters a person's sense of balance and causes false sensations of movement. EVS has been widely used to study the function of the vestibular system, and it has recently gained interest as a therapeutic tool to improve postural stability and help those suffering from vestibular dysfunction. Yet, understanding of how EVS stimulates the vestibular system, the current intensity needed to produce an effect and the frequencies at which it occurs have remained unclear.Approach. The effect of EVS on postural sway was examined in five participants using sinusoidal alternating current with time-varying amplitude from 0 to 1.5 mA and frequency from 0.1 to 10 Hz for three electrode configurations. Dosimetry of the current flow inside the head was conducted using anatomically realistic computational models created individually for each subject based on magnetic resonance imaging data. An estimate for the minimal field strength capable of affecting the vestibular system was calculated with the finite element method.Main results. Bipolar EVS at frequencies up to 10 Hz caused harmonic full-body swaying, and the frequency of the sway was the same as that of the stimulation current. The size of the sway was amplified by increasing the current intensity. Dosimetry modeling indicated that, for 0.2 mA current, the average electric field strength in the vestibular system was approximately 10-30 mV m-1, depending on the electrode montage. The size of the measured postural sway was proportional to the montage-specific electric field strength in the vestibular system.Significance. The results provide insight to EVS's working mechanisms and improve its potential as a tool to study the sense of balance.
{"title":"<i>In vivo</i>and dosimetric investigation on electrical vestibular stimulation with frequency- and amplitude-modulated currents.","authors":"Janita Nissi, Otto Kangasmaa, Juhani Kataja, Nicolas Bouisset, Ilkka Laakso","doi":"10.1088/1741-2552/ad658f","DOIUrl":"10.1088/1741-2552/ad658f","url":null,"abstract":"<p><p><i>Objective</i>. Normal function of the vestibular system can be disturbed using a noninvasive technique called electrical vestibular stimulation (EVS), which alters a person's sense of balance and causes false sensations of movement. EVS has been widely used to study the function of the vestibular system, and it has recently gained interest as a therapeutic tool to improve postural stability and help those suffering from vestibular dysfunction. Yet, understanding of how EVS stimulates the vestibular system, the current intensity needed to produce an effect and the frequencies at which it occurs have remained unclear.<i>Approach</i>. The effect of EVS on postural sway was examined in five participants using sinusoidal alternating current with time-varying amplitude from 0 to 1.5 mA and frequency from 0.1 to 10 Hz for three electrode configurations. Dosimetry of the current flow inside the head was conducted using anatomically realistic computational models created individually for each subject based on magnetic resonance imaging data. An estimate for the minimal field strength capable of affecting the vestibular system was calculated with the finite element method.<i>Main results</i>. Bipolar EVS at frequencies up to 10 Hz caused harmonic full-body swaying, and the frequency of the sway was the same as that of the stimulation current. The size of the sway was amplified by increasing the current intensity. Dosimetry modeling indicated that, for 0.2 mA current, the average electric field strength in the vestibular system was approximately 10-30 mV m<sup>-1</sup>, depending on the electrode montage. The size of the measured postural sway was proportional to the montage-specific electric field strength in the vestibular system.<i>Significance</i>. The results provide insight to EVS's working mechanisms and improve its potential as a tool to study the sense of balance.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728504","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}
Objective.Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.Approach.Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.Main results.Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.Significance.This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.
{"title":"A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces.","authors":"Jianjun Meng, Songwei Li, Guangye Li, Ruijie Luo, Xinjun Sheng, Xiangyang Zhu","doi":"10.1088/1741-2552/ad6595","DOIUrl":"10.1088/1741-2552/ad6595","url":null,"abstract":"<p><p><i>Objective.</i>Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.<i>Approach.</i>Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.<i>Main results.</i>Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.<i>Significance.</i>This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728499","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}
Pub Date : 2024-08-01DOI: 10.1088/1741-2552/ad6597
Andreas Fellner, Cornelia Wenger, Amirreza Heshmat, Frank Rattay
Objective. The cochlear implant (CI) belongs to the most successful neuro-prostheses. Traditionally, the stimulating electrode arrays are inserted into the scala tympani (ST), the lower cochlear cavity, which enables simple surgical access. However, often deep insertion is blocked, e.g. by ossification, and the auditory nerve fibers (ANFs) of lower frequency regions cannot be stimulated causing severe restrictions in speech understanding. As an alternative, the CI can be inserted into the scala vestibuli (SV), the other upper cochlear cavity.Approach. In this computational study, the excitability of 25 ANFs are compared for stimulation with ST and SV implants. We employed a 3-dimensional realistic human cochlear model with lateral wall electrodes based on aμ-CT dataset and manually traced fibers. A finite element approach in combination with a compartment model of a spiral ganglion cell was used to simulate monophasic stimulation with anodic (ANO) and cathodic (CAT) pulses of 50μs.Main results. ANO thresholds are lower in ST (mean/std =μ/σ= 189/55μA) stimulation compared to SV (μ/σ= 323/119μA) stimulation. Contrary, CAT thresholds are higher for the ST array (μ/σ= 165/42μA) compared to the SV array (μ/σ= 122/46μA). The threshold amplitude depends on the specific fiber-electrode spatial relationship, such as lateral distance from the cochlear axis, the angle between electrode and target ANF, and the curvature of the peripheral process. For CAT stimulation the SV electrodes show a higher selectivity leading to less cross-stimulation of additional fibers from different cochlear areas.Significance. We present a first simulation study with a human cochlear model that investigates an additional CI placement into the SV and its impact on the excitation behavior. Results predict comparable outcomes to ST electrodes which confirms that SV implantation might be an alternative for patients with a highly obstructed ST.
{"title":"Auditory nerve fiber excitability for alternative electrode placement in the obstructed human cochlea: electrode insertion in scala vestibuli versus scala tympani.","authors":"Andreas Fellner, Cornelia Wenger, Amirreza Heshmat, Frank Rattay","doi":"10.1088/1741-2552/ad6597","DOIUrl":"10.1088/1741-2552/ad6597","url":null,"abstract":"<p><p><i>Objective</i>. The cochlear implant (CI) belongs to the most successful neuro-prostheses. Traditionally, the stimulating electrode arrays are inserted into the scala tympani (ST), the lower cochlear cavity, which enables simple surgical access. However, often deep insertion is blocked, e.g. by ossification, and the auditory nerve fibers (ANFs) of lower frequency regions cannot be stimulated causing severe restrictions in speech understanding. As an alternative, the CI can be inserted into the scala vestibuli (SV), the other upper cochlear cavity.<i>Approach</i>. In this computational study, the excitability of 25 ANFs are compared for stimulation with ST and SV implants. We employed a 3-dimensional realistic human cochlear model with lateral wall electrodes based on a<i>μ</i>-CT dataset and manually traced fibers. A finite element approach in combination with a compartment model of a spiral ganglion cell was used to simulate monophasic stimulation with anodic (ANO) and cathodic (CAT) pulses of 50<i>μ</i>s.<i>Main results</i>. ANO thresholds are lower in ST (mean/std =<i>μ</i>/<i>σ</i>= 189/55<i>μ</i>A) stimulation compared to SV (<i>μ</i>/<i>σ</i>= 323/119<i>μ</i>A) stimulation. Contrary, CAT thresholds are higher for the ST array (<i>μ</i>/<i>σ</i>= 165/42<i>μ</i>A) compared to the SV array (<i>μ</i>/<i>σ</i>= 122/46<i>μ</i>A). The threshold amplitude depends on the specific fiber-electrode spatial relationship, such as lateral distance from the cochlear axis, the angle between electrode and target ANF, and the curvature of the peripheral process. For CAT stimulation the SV electrodes show a higher selectivity leading to less cross-stimulation of additional fibers from different cochlear areas.<i>Significance</i>. We present a first simulation study with a human cochlear model that investigates an additional CI placement into the SV and its impact on the excitation behavior. Results predict comparable outcomes to ST electrodes which confirms that SV implantation might be an alternative for patients with a highly obstructed ST.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728500","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}
Pub Date : 2024-08-01DOI: 10.1088/1741-2552/ad6189
Mads Jochumsen, Kathrin Battefeld Poulsen, Sascha Lan Sørensen, Cecilie Sørenbye Sulkjær, Frida Krogh Corydon, Laura Sølvberg Strauss, Julie Billingsø Roos
Objectives. Parkinson patients often suffer from motor impairments such as tremor and freezing of movement that can be difficult to treat. To unfreeze movement, it has been suggested to provide sensory stimuli. To avoid constant stimulation, episodes with freezing of movement needs to be detected which is a challenge. This can potentially be obtained using a brain-computer interface (BCI) based on movement-related cortical potentials (MRCPs) that are observed in association with the intention to move. The objective in this study was to detect MRCPs from single-trial EEG.Approach. Nine Parkinson patients executed 100 wrist movements and 100 ankle movements while continuous EEG and EMG were recorded. The experiment was repeated in two sessions on separate days. Using temporal, spectral and template matching features, a random forest (RF), linear discriminant analysis, and k-nearest neighbours (kNN) classifier were constructed in offline analysis to discriminate between epochs containing movement-related or idle brain activity to provide an estimation of the performance of a BCI. Three classification scenarios were tested: 1) within-session (using training and testing data from the same session and participant), between-session (using data from the same participant from session one for training and session two for testing), and across-participant (using data from all participants except one for training and testing on the remaining participant).Main results. The within-session classification scenario was associated with the highest classification accuracies which were in the range of 88%-89% with a similar performance across sessions. The performance dropped to 69%-75% and 70%-75% for the between-session and across-participant classification scenario, respectively. The highest classification accuracies were obtained for the RF and kNN classifiers.Significance. The results indicate that it is possible to detect movement intentions in individuals with Parkinson's disease such that they can operate a BCI which may control the delivery of sensory stimuli to unfreeze movement.
{"title":"Single-trial movement intention detection estimation in patients with Parkinson's disease: a movement-related cortical potential study.","authors":"Mads Jochumsen, Kathrin Battefeld Poulsen, Sascha Lan Sørensen, Cecilie Sørenbye Sulkjær, Frida Krogh Corydon, Laura Sølvberg Strauss, Julie Billingsø Roos","doi":"10.1088/1741-2552/ad6189","DOIUrl":"10.1088/1741-2552/ad6189","url":null,"abstract":"<p><p><i>Objectives</i>. Parkinson patients often suffer from motor impairments such as tremor and freezing of movement that can be difficult to treat. To unfreeze movement, it has been suggested to provide sensory stimuli. To avoid constant stimulation, episodes with freezing of movement needs to be detected which is a challenge. This can potentially be obtained using a brain-computer interface (BCI) based on movement-related cortical potentials (MRCPs) that are observed in association with the intention to move. The objective in this study was to detect MRCPs from single-trial EEG.<i>Approach</i>. Nine Parkinson patients executed 100 wrist movements and 100 ankle movements while continuous EEG and EMG were recorded. The experiment was repeated in two sessions on separate days. Using temporal, spectral and template matching features, a random forest (RF), linear discriminant analysis, and k-nearest neighbours (kNN) classifier were constructed in offline analysis to discriminate between epochs containing movement-related or idle brain activity to provide an estimation of the performance of a BCI. Three classification scenarios were tested: 1) within-session (using training and testing data from the same session and participant), between-session (using data from the same participant from session one for training and session two for testing), and across-participant (using data from all participants except one for training and testing on the remaining participant).<i>Main results</i>. The within-session classification scenario was associated with the highest classification accuracies which were in the range of 88%-89% with a similar performance across sessions. The performance dropped to 69%-75% and 70%-75% for the between-session and across-participant classification scenario, respectively. The highest classification accuracies were obtained for the RF and kNN classifiers.<i>Significance</i>. The results indicate that it is possible to detect movement intentions in individuals with Parkinson's disease such that they can operate a BCI which may control the delivery of sensory stimuli to unfreeze movement.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581935","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}
Pub Date : 2024-07-31DOI: 10.1088/1741-2552/ad5936
Quentin Whitsitt, Akash Saxena, Bella Patel, Blake M Evans, Bradley Hunt, Erin K Purcell
Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state.Objective.To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality.Approach.For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant.Main results. Results from rat motor cortex within 300µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6-7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes.Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.
{"title":"Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality.","authors":"Quentin Whitsitt, Akash Saxena, Bella Patel, Blake M Evans, Bradley Hunt, Erin K Purcell","doi":"10.1088/1741-2552/ad5936","DOIUrl":"10.1088/1741-2552/ad5936","url":null,"abstract":"<p><p>Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state.<i>Objective.</i>To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality.<i>Approach.</i>For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant.<i>Main results</i>. Results from rat motor cortex within 300<i>µ</i>m of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6-7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes.<i>Significance</i>. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422311","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}
Pub Date : 2024-07-31DOI: 10.1088/1741-2552/ad6598
A G Habashi, Ahmed M Azab, Seif Eldawlatly, Gamal M Aly
Objective.Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technology.Approach.This study focuses on enhancing cross-subject (CS) MI EEG classification using EEG spectrum images. The proposed calibration-free approach employs deep learning techniques for MI classification and Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed WGAN generates synthetic spectrum images from the recorded MI-EEG to expand the training dataset; aiming to enhance the classifier's performance. The proposed approach eliminates the need for any calibration data from the target subject, making it more suitable for real-world applications.Main results.To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2 A, and IV-1 benchmark datasets, employing leave one-subject out validation. Our results demonstrate that using the proposed modified VGG-CNN classifier in addition to WGAN-generated data for augmentation leads to an enhancement in CS accuracy outperforming state-of-the-art methods.Significance.This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications.
目的:运动想象(MI)是脑机接口(BCI)的一个主要范例,其中用户依靠脑电图(EEG)信号来控制物体的运动。然而,由于受试者之间存在差异,MI BCI 需要记录受试者的相关数据,以训练机器学习分类器,用于识别预期的运动动作。这对 MI BCI 的开发是一个挑战,因为它使校准变得复杂,并阻碍了这种技术的广泛应用:本研究的重点是利用脑电图频谱图像加强跨主体 MI 脑电图分类。所提出的免校准方法采用深度学习技术进行 MI 分类,并采用 Wasserstein 生成对抗网络(WGAN)进行数据增强。拟议的 WGAN 可从记录的 MI-EEG 生成合成频谱图像,以扩展训练数据集,从而提高分类器的性能。所提出的方法无需目标对象的任何校准数据,因此更适合真实世界的应用:为了评估所提框架的稳健性和有效性,我们利用了BCI竞赛IV-2B、IV-2A和IV-1基准数据集,并进行了单对象排除验证。我们的研究结果表明,除了使用 WGAN 生成的数据进行增强外,使用所提出的改进型 VGG-CNN 分类器还能提高跨受试者准确性,其准确性优于最先进的方法:意义:这种方法代表着向开发免校准 BCI 系统迈进了一步,从而扩大了其应用范围。
{"title":"Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.","authors":"A G Habashi, Ahmed M Azab, Seif Eldawlatly, Gamal M Aly","doi":"10.1088/1741-2552/ad6598","DOIUrl":"10.1088/1741-2552/ad6598","url":null,"abstract":"<p><p><i>Objective.</i>Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technology.<i>Approach.</i>This study focuses on enhancing cross-subject (CS) MI EEG classification using EEG spectrum images. The proposed calibration-free approach employs deep learning techniques for MI classification and Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed WGAN generates synthetic spectrum images from the recorded MI-EEG to expand the training dataset; aiming to enhance the classifier's performance. The proposed approach eliminates the need for any calibration data from the target subject, making it more suitable for real-world applications.<i>Main results.</i>To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2 A, and IV-1 benchmark datasets, employing leave one-subject out validation. Our results demonstrate that using the proposed modified VGG-CNN classifier in addition to WGAN-generated data for augmentation leads to an enhancement in CS accuracy outperforming state-of-the-art methods.<i>Significance.</i>This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728409","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}
Objective. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, during surgery electroencephalography (EEG) is usually subject to various disturbances that affect the accuracy of DOA. Therefore, accurately estimating noise in EEG and reliably assessing DOA remains an important challenge. In this paper, we proposed a signal quality index (SQI) network (SQINet) for assessing the EEG signal quality and a DOA network (DOANet) for analyzing EEG signals to precisely estimate DOA. The two networks are termed SQI-DOANet.Approach. The SQINet contained a shallow convolutional neural network to quickly determine the quality of the EEG signal. The DOANet comprised a feature extraction module for extracting features, a dual attention module for fusing multi-channel and multi-scale information, and a gated multilayer perceptron module for extracting temporal information. The performance of the SQI-DOANet model was validated by training and testing the model on the large VitalDB database, with the bispectral index (BIS) as the reference standard.Main results. The proposed DOANet yielded a Pearson correlation coefficient with the BIS score of 0.88 in the five-fold cross-validation, with a mean absolute error (MAE) of 4.81. The mean Pearson correlation coefficient of SQI-DOANet with the BIS score in the five-fold cross-validation was 0.82, with an MAE of 5.66.Significance. The SQI-DOANet model outperformed three compared methods. The proposed SQI-DOANet may be used as a new deep learning method for DOA estimation. The code of the SQI-DOANet will be made available publicly athttps://github.com/YuRui8879/SQI-DOANet.
目的:
在手术过程中监测麻醉深度(DOA)至关重要。然而,准确、实时地估计 DOA 仍然是一项具有挑战性的任务。在本文中,我们提出了一个用于评估脑电图(EEG)信号质量的信号质量指标(SQI)网络(SQINet)和一个用于分析 EEG 信号以精确估计 DOA 的 DOA 网络(DOANet)。这两个网络被称为 SQI-DOANet。
方法:
SQINet 包含一个浅层卷积神经网络,用于快速确定脑电信号的质量。DOANet 包括用于提取特征的特征提取模块、用于融合多通道和多尺度信息的双注意模块,以及用于提取时间信息的门控多层感知器模块。以双谱指数(BIS)为参考标准,在大型 VitalDB 数据库上对 SQI-DOANet 模型进行了训练和测试,从而验证了该模型的性能。在 5 倍交叉验证中,SQI-DOANet 与 BIS 评分的平均皮尔逊相关系数为 0.82,平均绝对误差为 5.66。提出的 SQI-DOANet 可作为一种新的深度学习方法用于 DOA 估计。SQI-DOANet 的代码将在 https://github.com/YuRui8879/SQI-DOANet.
公开。
{"title":"SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia.","authors":"Rui Yu, Zhuhuang Zhou, Meng Xu, Meng Gao, Meitong Zhu, Shuicai Wu, Xiaorong Gao, Guangyu Bin","doi":"10.1088/1741-2552/ad6592","DOIUrl":"10.1088/1741-2552/ad6592","url":null,"abstract":"<p><p><i>Objective</i>. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, during surgery electroencephalography (EEG) is usually subject to various disturbances that affect the accuracy of DOA. Therefore, accurately estimating noise in EEG and reliably assessing DOA remains an important challenge. In this paper, we proposed a signal quality index (SQI) network (SQINet) for assessing the EEG signal quality and a DOA network (DOANet) for analyzing EEG signals to precisely estimate DOA. The two networks are termed SQI-DOANet.<i>Approach</i>. The SQINet contained a shallow convolutional neural network to quickly determine the quality of the EEG signal. The DOANet comprised a feature extraction module for extracting features, a dual attention module for fusing multi-channel and multi-scale information, and a gated multilayer perceptron module for extracting temporal information. The performance of the SQI-DOANet model was validated by training and testing the model on the large VitalDB database, with the bispectral index (BIS) as the reference standard.<i>Main results</i>. The proposed DOANet yielded a Pearson correlation coefficient with the BIS score of 0.88 in the five-fold cross-validation, with a mean absolute error (MAE) of 4.81. The mean Pearson correlation coefficient of SQI-DOANet with the BIS score in the five-fold cross-validation was 0.82, with an MAE of 5.66.<i>Significance</i>. The SQI-DOANet model outperformed three compared methods. The proposed SQI-DOANet may be used as a new deep learning method for DOA estimation. The code of the SQI-DOANet will be made available publicly athttps://github.com/YuRui8879/SQI-DOANet.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728508","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}
Pub Date : 2024-07-29DOI: 10.1088/1741-2552/ad6594
Kyriaki Kostoglou, Konstantinos P Michmizos, Pantelis Stathis, Damianos Sakas, Konstantina S Nikita, Georgios D Mitsis
Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.
{"title":"Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials.","authors":"Kyriaki Kostoglou, Konstantinos P Michmizos, Pantelis Stathis, Damianos Sakas, Konstantina S Nikita, Georgios D Mitsis","doi":"10.1088/1741-2552/ad6594","DOIUrl":"10.1088/1741-2552/ad6594","url":null,"abstract":"<p><p><i>Objective.</i>Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.<i>Approach.</i>Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.<i>Main results.</i>The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.<i>Significance.</i>These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728507","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}
Pub Date : 2024-07-29DOI: 10.1088/1741-2552/ad5ebf
Irene Mendez Guerra, Deren Y Barsakcioglu, Dario Farina
Objective. Developing neural decoders robust to non-stationary conditions is essential to ensure their long-term accuracy and stability. This is particularly important when decoding the neural drive to muscles during dynamic contractions, which pose significant challenges for stationary decoders.Approach. We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Leibler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (∼22 ms of computational time per 100 ms batches).Main results. The hyperparameters of the proposed adaptation captured anatomical differences between recording locations (forearm vs wrist) and generalised across subjects. Once optimised, the proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80∘). The rate of agreement, sensitivity, and precision were⩾90%in⩾80%of the cases in both simulated and experimentally recorded data, according to a two-source validation approach.Significance. The findings demonstrate the suitability of the proposed online learning metrics and hyperparameter optimisation to compensate the induced modulation and accurately decode MN discharges in dynamic conditions. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.
{"title":"Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters.","authors":"Irene Mendez Guerra, Deren Y Barsakcioglu, Dario Farina","doi":"10.1088/1741-2552/ad5ebf","DOIUrl":"10.1088/1741-2552/ad5ebf","url":null,"abstract":"<p><p><i>Objective</i>. Developing neural decoders robust to non-stationary conditions is essential to ensure their long-term accuracy and stability. This is particularly important when decoding the neural drive to muscles during dynamic contractions, which pose significant challenges for stationary decoders.<i>Approach</i>. We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Leibler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (∼22 ms of computational time per 100 ms batches).<i>Main results</i>. The hyperparameters of the proposed adaptation captured anatomical differences between recording locations (forearm vs wrist) and generalised across subjects. Once optimised, the proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80<sup>∘</sup>). The rate of agreement, sensitivity, and precision were⩾90%in⩾80%of the cases in both simulated and experimentally recorded data, according to a two-source validation approach.<i>Significance</i>. The findings demonstrate the suitability of the proposed online learning metrics and hyperparameter optimisation to compensate the induced modulation and accurately decode MN discharges in dynamic conditions. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500038","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}