JournalTOCs

Frontiers in Neuroprosthetics
Number of Followers: 6

This is an Open Access Journal

Open Access journal
ISSN (Online) 1662-9957
Published by Frontiers Media

[96 journals]

Motor Imagery-Related Changes of Neural Oscillation in Unilateral Lower
Limb Amputation
- Authors: Xinying Shan, Jialu Li, Lingjing Zeng, Haiteng Wang, Tianyi Yang, Yongcong Shao, Mengsun Yu
  Abstract: An amputation is known to seriously affect patient quality of life. This study aimed to investigate changes in neural activity in amputees during the postoperative period using neural electrophysiological techniques. In total, 14 patients with left lower limb amputation and 18 healthy participants were included in our study. All participants were required to perform motor imagery paradigm tasks while electroencephalogram (EEG) data were recorded. Data analysis results indicated that the beta frequency band showed significantly decreased oscillatory activity in motor imaging-related brain regions such as the frontal lobe and the precentral and postcentral gyri in amputees. Furthermore, the functional independent component analysis (fICA) value of neural oscillation negatively correlated with the C4 electrode power value of the motor imagery task in amputees (p < 0.05). Therefore, changes in neural oscillations and beta frequency band in motor imagery regions may be related to brain remodeling in amputees.
  PubDate: 2022-05-19T00:00:00Z
Sonification of Computer Processes: The Cases of Computer Shutdown and
Idle Mode
- Authors: Claudio Panariello, Roberto Bresin
  Abstract: Software is intangible, invisible, and at the same time pervasive in everyday devices, activities, and services accompanying our life. Therefore, citizens hardly realize its complexity, power, and impact in many aspects of their daily life. In this study, we report on one experiment that aims at letting citizens make sense of software presence and activity in their everyday lives, through sound: the invisible complexity of the processes involved in the shutdown of a personal computer. We used sonification to map information embedded in software events into the sound domain. The software events involved in a shutdown have names related to the physical world and its actions: write events (information is saved into digital memories), kill events (running processes are terminated), and exit events (running programs are exited). The research study presented in this article has a “double character.” It is an artistic realization that develops specific aesthetic choices, and it has also pedagogical purposes informing the causal listener about the complexity of software behavior. Two different sound design strategies have been applied: one strategy is influenced by the sonic characteristics of the Glitch music scene, which makes deliberate use of glitch-based sound materials, distortions, aliasing, quantization noise, and all the “failures” of digital technologies; and a second strategy based on the sound samples of a subcontrabass Paetzold recorder, an unusual and special acoustic instrument which unique sound has been investigated in the contemporary art music scene. Analysis of quantitative ratings and qualitative comments of 37 participants revealed that the sound design strategies succeeded in communicating the nature of the computer processes. Participants also showed in general an appreciation of the aesthetics of the peculiar sound models used in this study.
  PubDate: 2022-05-04T00:00:00Z
Increasing Robustness of Brain–Computer Interfaces Through Automatic
Detection and Removal of Corrupted Input Signals
- Authors: Jordan L. Vasko, Laura Aume, Sanjay Tamrakar, Samuel C. IV Colachis, Collin F. Dunlap, Adam Rich, Eric C. Meyers, David Gabrieli, David A. Friedenberg
  Abstract: For brain–computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.
  PubDate: 2022-04-28T00:00:00Z
Subclinical Atherosclerosis Could Increase the Risk of Hearing Impairment
in Males: A Community-Based Cross-Sectional Survey of the Kailuan
Study|Objective|Methods|Results|Conclusion
- Authors: Chunyu Ruan, Xiang Mao, Shuohua Chen, Shouling Wu, Wei Wang
  Abstract: ObjectiveThe relationship between subclinical atherosclerosis and hearing impairment (HI) has not been widely considered. Brachial ankle pulse wave velocity (baPWV) is a good indicator of muscular artery elasticity and could be a feasible method to screen for subclinical atherosclerosis. Our study aimed to elucidate the relationship between baPWV and HI.MethodsThis cross-sectional study was based on the Kailuan cohort. All participants completed a standardized questionnaire and underwent physical examinations and laboratory assessments at recruitment. Since 2010, some participants received additional baPWV testing during follow-up visits, and some who were exposed to occupational hazards such as noise received a pure-tone average hearing threshold (PTA) test after 2014. Male subjects with a complete physical examination, baPWV, and PTA data were recruited for this study. HI was defined as PTA> 25 dB. Multivariate linear and multivariate logistic regression analyses were used to evaluate the relationship between baPWV and PTA or HI.ResultsAmong 11,141 subjects, the age range was 18–65 years, with mean age of 43.3 ± 8.9 years, the average PTA was 20.54 ± 10.40 dB, and the detection rate of HI was 1,821/11,141 (16.3%). Subjects were divided into four subgroups according to baPWV quartile. As the baPWV quartile increased, age, systolic blood pressure, diastolic blood pressure, body mass index, total cholesterol, high-density-lipoprotein cholesterol, fasting blood glucose, PTA, and proportions of subjects reporting smoking, alcohol consumption, hypertension, and diabetes increased significantly (p < 0.05 for trend). The odds of HI were higher in the fourth quartile group [adjusted odds ratio (aOR): 1.33, 95% CI: 1.10–1.62] than in the first quartile group. For every 100 m/s increase in baPWV, the PTA increased by 13 dB (95% CI: 4–23). When we divided the subjects into young (5,478 subjects; age range 22–44 years; mean age 35.6 ± 5.5 years) or non-young subgroups (5,663 subjects; age range 45–65 years; mean age 50.7 ± 3.7 years) based on a cut-off age of 45 years, the aOR of the fourth quartile group increased to 2.65 (95% CI: 1.68–4.19), and the PTA increment increased to 18 dB (95% CI: 10–27) for every 100 m/s increase in baPWV in the young subgroup. However, this relationship became statistically insignificant in the non-young subgroup.ConclusionOur study revealed the quantitative relationship between baPWV and HI in the Kailuan cohort subjects, although the results are not universally consistent in different populations.
  PubDate: 2022-04-25T00:00:00Z
Editorial: Current Trends in Deep Learning for Movement Analysis and
Prosthesis Control
- Authors: Ali H. Al-Timemy, Claudio Castellini, Javier Escudero, Rami Khushaba, Silvia Muceli
  PubDate: 2022-04-25T00:00:00Z
Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction
Through a Dense Co-Attention Symmetric Mechanism
- Authors: Kecheng Shi, Fengjun Mu, Rui Huang, Ke Huang, Zhinan Peng, Chaobin Zou, Xiao Yang, Hong Cheng
  Abstract: A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited—the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.
  PubDate: 2022-04-25T00:00:00Z
Transcranial Magnetic Stimulation for Improving Dysphagia After Stroke: A
Meta-Analysis of Randomized Controlled
Trials|Background|Methods|Results|Conclusion
- Authors: Yu-lei Xie, Shan Wang, Jia-meng Jia, Yu-han Xie, Xin Chen, Wu Qing, Yin-xu Wang
  Abstract: BackgroundRehabilitation of post-stroke dysphagia is an urgent clinical problem, and repetitive transcranial magnetic stimulation (rTMS) has been widely used in the study of post-stroke function. However, there is no reliable evidence-based medicine to support the effect of rTMS on post-stroke dysphagia. This review aims to evaluate the effectiveness and safety of rTMS on post-stroke dysphagia.MethodsEnglish-language literature published before December 20, 2021, were searched in six electronic databases. Identified articles were screened, data were extracted, and the methodological quality of included trials was assessed. Meta-analysis was performed using RevMan 5.3 software. The GRADE method was used to assess the quality of the evidence.ResultsA total of 10 studies with 246 patients were included. Meta-analysis showed that rTMS significantly improved overall swallowing function (standardized mean difference [SMD]−0.76, 95% confidence interval (CI)−1.07 to−0.46, p < 0.0001, n = 206; moderate-quality evidence), Penetration Aspiration Scale (PAS) (mean difference [MD]−1.03, 95% CI−1.51 to−0.55, p < 0.0001, n = 161; low-quality evidence) and Barthel index scale (BI) (MD 23.86, 95% CI 12.73 to 34.99, p < 0.0001, n = 136; moderate-quality evidence). Subgroup analyses revealed that (1) rTMS targeting the affected hemisphere and targeting both hemispheres significantly enhanced overall swallowing function and reduced aspiration. (2) Low-frequency rTMS significantly enhanced overall swallowing function and reduced aspiration, and there was no significant difference between high-frequency rTMS and control group in reducing aspiration (p = 0.09). (3) There was no statistical difference in the dropout rate (low-quality evidence) and adverse effects (moderate-quality evidence) between the rTMS group and the control group.ConclusionrTMS improved overall swallowing function and activity of daily living ability and reduced aspiration in post-stroke patients with good acceptability and mild adverse effects.
  PubDate: 2022-04-22T00:00:00Z
Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA
- Authors: Yongqiang Dai, Lili Niu, Linjing Wei, Jie Tang
  Abstract: High-dimensional biomedical data contained many irrelevant or weakly correlated features, which affected the efficiency of disease diagnosis. This manuscript presented a feature selection method for high-dimensional biomedical data based on the chemotaxis foraging-shuffled frog leaping algorithm (BF-SFLA). The performance of the BF-SFLA based feature selection method was further improved by introducing chemokine operation and balanced grouping strategies into the shuffled frog leaping algorithm, which maintained the balance between global optimization and local optimization and reduced the possibility of the algorithm falling into local optimization. To evaluate the proposed method’s effectiveness, we employed the K-NN (k-nearest Neighbor) and C4.5 decision tree classification algorithm with a comparative analysis. We compared our proposed approach with improved genetic algorithms, particle swarm optimization, and the basic shuffled frog leaping algorithm. Experimental results showed that the feature selection method based on BF-SFLA obtained a better feature subset, improved classification accuracy, and shortened classification time.
  PubDate: 2022-04-18T00:00:00Z
Classification of Whisker Deflections From Evoked Responses in the
Somatosensory Barrel Cortex With Spiking Neural Networks
- Authors: Horst Petschenig, Marta Bisio, Marta Maschietto, Alessandro Leparulo, Robert Legenstein, Stefano Vassanelli
  Abstract: Spike-based neuromorphic hardware has great potential for low-energy brain-machine interfaces, leading to a novel paradigm for neuroprosthetics where spiking neurons in silicon read out and control activity of brain circuits. Neuromorphic processors can receive rich information about brain activity from both spikes and local field potentials (LFPs) recorded by implanted neural probes. However, it was unclear whether spiking neural networks (SNNs) implemented on such devices can effectively process that information. Here, we demonstrate that SNNs can be trained to classify whisker deflections of different amplitudes from evoked responses in a single barrel of the rat somatosensory cortex. We show that the classification performance is comparable or even superior to state-of-the-art machine learning approaches. We find that SNNs are rather insensitive to recorded signal type: both multi-unit spiking activity and LFPs yield similar results, where LFPs from cortical layers III and IV seem better suited than those of deep layers. In addition, no hand-crafted features need to be extracted from the data—multi-unit activity can directly be fed into these networks and a simple event-encoding of LFPs is sufficient for good performance. Furthermore, we find that the performance of SNNs is insensitive to the network state—their performance is similar during UP and DOWN states.
  PubDate: 2022-04-14T00:00:00Z
Towards User-Centred Prosthetics Research Beyond the Laboratory
- Authors: Hannah Jones, Lynda Webb, Matthew Dyson, Kianoush Nazarpour
  Abstract: The purpose of this study was to explore a range of perspectives on how academic research and clinical assessment of upper-limb prosthetics could happen in environments outside of laboratories and clinics, such as within peoples’ homes. Two co-creation workshops were held, which included people who use upper limb prosthetic devices (hereafter called users), clinicians, academics, a policy stakeholder, and a representative from the upper-limb prosthetics industry (hereafter called professionals). The discussions during the workshops indicate that research and clinical assessment conducted remotely from a laboratory or clinic could inform future solutions that address user needs. Users were open to the idea of sharing sensor and contextual data from within their homes to external laboratories during research studies. However, this was dependent upon several considerations, such as choice and control over data collection. Regarding clinical assessment, users had reservations of how data may be used to inform future prosthetic prescriptions whilst, clinicians were concerned with resource implications and capacity to process user data. The paper presents findings of the discussions shared by participants during both workshops. The paper concludes with a conjecture that collecting sensor and contextual data from users within their home environment will contribute towards literature within the field, and potentially inform future care policies for upper limb prosthetics. The involvement of users during such studies will be critical and can be enabled via a co-creation approach. In the short term, this may be achieved through academic research studies, which may in the long term inform a framework for clinical in-home trials and clinical remote assessment.
  PubDate: 2022-04-14T00:00:00Z
Clinical Basis for Creating an Osseointegrated Neural Interface
- Authors: Alison M. Karczewski, Weifeng Zeng, Lindsay M. Stratchko, Kent N. Bachus, Samuel O. Poore, Aaron M. Dingle
  Abstract: As technology continues to improve within the neuroprosthetic landscape, there has been a paradigm shift in the approach to amputation and surgical implementation of haptic neural prosthesis for limb restoration. The Osseointegrated Neural Interface (ONI) is a proposed solution involving the transposition of terminal nerves into the medullary canal of long bones. This design combines concepts of neuroma formation and prevention with osseointegration to provide a stable environment for conduction of neural signals for sophisticated prosthetic control. While this concept has previously been explored in animal models, it has yet to be explored in humans. This anatomic study used three upper limb and three lower limb cadavers to assess the clinical feasibility of creating an ONI in humans. Anatomical measurement of the major peripheral nerves- circumference, length, and depth- were performed as they are critical for electrode design and rerouting of the nerves into the long bones. CT imaging was used for morphologic bone evaluation and virtual implantation of two osseointegrated implants were performed to assess the amount of residual medullary space available for housing the neural interfacing hardware. Use of a small stem osseointegrated implant was found to reduce bone removal and provide more intramedullary space than a traditional implant; however, the higher the amputation site, the less medullary space was available regardless of implant type. Thus the stability of the endoprosthesis must be maximized while still maintaining enough residual space for the interface components. The results from this study provide an anatomic basis required for establishing a clinically applicable ONI in humans. They may serve as a guide for surgical implementation of an osseointegrated endoprosthesis with intramedullary electrodes for prosthetic control.
  PubDate: 2022-04-12T00:00:00Z
Brain–Computer Interface-Robot Training Enhances Upper Extremity
Performance and Changes the Cortical Activation in Stroke Patients: A
Functional Near-Infrared Spectroscopy
- Authors: Lingyu Liu, Minxia Jin, Linguo Zhang, Qiuzhen Zhang, Dunrong Hu, Lingjing Jin, Zhiyu Nie
  Abstract: IntroductionWe evaluated the efficacy of brain–computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M1) after BCI training.MethodsIn this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.ResultsCompared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (ΔWMFT score = 12.39 points, F = 30.28, and P < 0.001), WMFT completion time (ΔWMFT time = 248.39 s, F = 16.83, and P < 0.001), and FMA full score (ΔFMA-UE = 12.72 points, F = 106.07, and P < 0.001), FMA-WH sub-score (ΔFMA-WH = 5.6 points, F = 35.53, and P < 0.001), and FMA-SE sub-score (ΔFMA-SE = 8.06 points, F = 22.38, and P < 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M1 and the contralateral M1 was increased (P < 0.05), which was the same as the FC between the ipsilateral M1 and the ipsilateral frontal lobe, and the FC between the contralateral M1 and the contralateral frontal lobe was also increased (P < 0.05).ConclusionThe findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M1 and frontal cortex might be enhanced after BCI training.Clinical Trial Registrationwww.chictr.org.cn, identifier: ChiCTR2100046301.
  PubDate: 2022-04-08T00:00:00Z
Spike-Representation of EEG Signals for Performance Enhancement of
Brain-Computer Interfaces
- Authors: Sai Kalyan Ranga Singanamalla, Chin-Teng Lin
  Abstract: Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. But EEG signal is the result of aggregation of neuronal spiking information underneath the scalp surface and transforming the relevant BCI task's EEG signal to spike representation could potentially help improve the BCI performance. In this study, we designed an approach using a spiking neural network (SNN) which is trained using surrogate-gradient descent to generate task-related multi-channel EEG template signals of all classes. The trained model is in turn leveraged to obtain the latent spike representation for each EEG sample. Comparing the classification performance of EEG signal and its spike-representation, the proposed approach enhanced the performance of ERN dataset from 79.22 to 82.27% with naive bayes and for P300 dataset, the accuracy was improved from 67.73 to 69.87% using xGboost. In addition, principal component analysis and correlation metrics were evaluated on both EEG signals and their spike-representation to identify the reason for such improvement.
  PubDate: 2022-04-04T00:00:00Z
The Frequency Effect of the Motor Imagery Brain Computer Interface
Training on Cortical Response in Healthy Subjects: A Randomized Clinical
Trial of Functional Near-Infrared Spectroscopy
Study|Background|Methods|Results|Conclusion
- Authors: Qiang Lin, Yanni Zhang, Yajie Zhang, Wanqi Zhuang, Biyi Zhao, Xiaomin Ke, Tingting Peng, Tingting You, Yongchun Jiang, Anniwaer Yilifate, Wei Huang, Lingying Hou, Yaoyao You, Yaping Huai, Yaxian Qiu, Yuxin Zheng, Haining Ou
  Abstract: BackgroundThe motor imagery brain computer interface (MI-BCI) is now available in a commercial product for clinical rehabilitation. However, MI-BCI is still a relatively new technology for commercial rehabilitation application and there is limited prior work on the frequency effect. The MI-BCI has become a commercial product for clinical neurological rehabilitation, such as rehabilitation for upper limb motor dysfunction after stroke. However, the formulation of clinical rehabilitation programs for MI-BCI is lack of scientific and standardized guidance, especially limited prior work on the frequency effect. Therefore, this study aims at clarifying how frequency effects on MI-BCI training for the plasticity of the central nervous system.MethodsSixteen young healthy subjects (aged 22.94 ± 3.86 years) were enrolled in this randomized clinical trial study. Subjects were randomly assigned to a high frequency group (HF group) and low frequency group (LF group). The HF group performed MI-BCI training once per day while the LF group performed once every other day. All subjects performed 10 sessions of MI-BCI training. functional near-infrared spectroscopy (fNIRS) measurement, Wolf Motor Function Test (WMFT) and brain computer interface (BCI) performance were assessed at baseline, mid-assessment (after completion of five BCI training sessions), and post-assessment (after completion of 10 BCI training sessions).ResultsThe results from the two-way ANOVA of beta values indicated that GROUP, TIME, and GROUP × TIME interaction of the right primary sensorimotor cortex had significant main effects [GROUP: F(1,14) = 7.251, P = 0.010; TIME: F(2,13) = 3.317, P = 0.046; GROUP × TIME: F(2,13) = 5.676, P = 0.007]. The degree of activation was affected by training frequency, evaluation time point and interaction. The activation of left primary sensory motor cortex was also affected by group (frequency) (P = 0.003). Moreover, the TIME variable was only significantly different in the HF group, in which the beta value of the mid-assessment was higher than that of both the baseline assessment (P = 0.027) and post-assessment (P = 0.001), respectively. Nevertheless, there was no significant difference in the results of WMFT between HF group and LF group.ConclusionThe major results showed that more cortical activation and better BCI performance were found in the HF group relative to the LF group. Moreover, the within-group results also showed more cortical activation after five sessions of BCI training and better BCI performance after 10 sessions in the HF group, but no similar effects were found in the LF group. This pilot study provided an essential reference for the formulation of clinical programs for MI-BCI training in improvement for upper limb dysfunction.
  PubDate: 2022-03-31T00:00:00Z
Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using
Three-Dimensional Convolutional Neural Network
- Authors: Yaser Fathi, Abbas Erfanian
  Abstract: To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.
  PubDate: 2022-03-25T00:00:00Z
An Anodic Phase Can Facilitate Rather Than Weaken a Cathodic Phase to
Activate Neurons in Biphasic-Pulse Axonal Stimulations
- Authors: Lvpiao Zheng, Zhouyan Feng, Yipeng Xu, Yue Yuan, Yifan Hu
  Abstract: Electrical pulses have been promisingly utilized in neural stimulations to treat various diseases. Usually, charge-balanced biphasic pulses are applied in the clinic to eliminate the possible side effects caused by charge accumulations. Because of its reversal action to the preceding cathodic phase, the subsequent anodic phase has been commonly considered to lower the activation efficiency of biphasic pulses. However, an anodic pulse itself can also activate axons with its “virtual cathode” effect. Therefore, we hypothesized that the anodic phase of a biphasic pulse could facilitate neuronal activation in some circumstances. To verify the hypothesis, we compared the activation efficiencies of cathodic pulse, biphasic pulse, and anodic pulse applied in both monopolar and bipolar modes in the axonal stimulation of alveus in rat hippocampal CA1 region in vivo. The antidromically evoked population spikes (APS) were recorded and used to evaluate the amount of integrated firing of pyramidal neurons induced by pulse stimulations. We also used a computational model to investigate the pulse effects on axons at various distances from the stimulation electrode. The experimental results showed that, with a small pulse intensity, a cathodic pulse recruited more neurons to fire than a biphasic pulse. However, the situation was reversed with an increased pulse intensity. In addition, setting an inter-phase gap of 100 μs was able to increase the activation efficiency of a biphasic pulse to exceed a cathodic pulse even with a relatively small pulse intensity. Furthermore, the latency of APS evoked by a cathodic pulse was always longer than that of APS evoked by a biphasic pulse, indicating different initial sites of the neuronal firing evoked by the different types of pulses. The computational results of axon modeling showed that the subsequent anodic phase was able to relieve the hyperpolarization block in the flanking regions generated by the preceding cathodic phase, thereby increasing rather than decreasing the activation efficiency of a biphasic pulse with a relatively great intensity. These results of both rat experiments and computational modeling firstly reveal a facilitation rather than an attenuation effect of the anodic phase on biphasic-pulse stimulations, which provides important information for designing electrical stimulations for neural therapies.
  PubDate: 2022-03-17T00:00:00Z
Spontaneous State Detection Using Time-Frequency and Time-Domain Features
Extracted From Stereo-Electroencephalography Traces
- Authors: Huanpeng Ye, Zhen Fan, Guangye Li, Zehan Wu, Jie Hu, Xinjun Sheng, Liang Chen, Xiangyang Zhu
  Abstract: As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60–140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.
  PubDate: 2022-03-17T00:00:00Z
Functional Connectivity Analysis and Detection of Mental Fatigue Induced
by Different Tasks Using Functional Near-Infrared
Spectroscopy|Objectives|Methods|Results|Conclusion
- Authors: Yaoxing Peng, Chunguang Li, Qu Chen, Yufei Zhu, Lining Sun
  Abstract: ObjectivesThe objective of this study was to investigate common functional near-infrared spectroscopy (fNIRS) features of mental fatigue induced by different tasks. In addition to distinguishing fatigue from non-fatigue state, the early signs of fatigue were also studied so as to give an early warning of fatigue.MethodsfNIRS data from 36 participants were used to investigate the common character of functional connectivity network corresponding to mental fatigue, which was induced by psychomotor vigilance test (PVT), cognitive work, or simulated driving. To analyze the network reorganizations quantitatively, clustering coefficient, characteristic path length, and small worldness were calculated in five sub-bands (0.6–2.0, 0.145–0.600, 0.052–0.145, 0.021–0.052, and 0.005–0.021 Hz). Moreover, we applied a random forest method to classify three fatigue states.ResultsIn a moderate fatigue state: the functional connectivity strength between brain regions increased overall in 0.021–0.052 Hz, and an asymmetrical pattern of connectivity (right hemisphere > left hemisphere) was presented. In 0.052–0.145 Hz, the connectivity strength decreased overall, the clustering coefficient decreased, and the characteristic path length increased significantly. In severe fatigue state: in 0.021–0.052 Hz, the brain network began to deviate from a small-world pattern. The classification accuracy of fatigue and non-fatigue was 85.4%. The classification accuracy of moderate fatigue and severe fatigue was 82.8%.ConclusionThe preliminary research demonstrates the feasibility of detecting mental fatigue induced by different tasks, by applying the functional network features of cerebral hemoglobin signal. This universal and robust method has the potential to detect early signs of mental fatigue and prevent relative human error in various working environments.
  PubDate: 2022-03-15T00:00:00Z
Corrigendum: The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset
of Healthy and Pathological EEG Recordings for Predictive Modeling
- Authors: Hassan Aqeel Khan, Rahat Ul Ain, Awais Mehmood Kamboh, Hammad Tanveer Butt, Saima Shafait, Wasim Alamgir, Didier Stricker, Faisal Shafait
  PubDate: 2022-03-15T00:00:00Z
Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor
Observation and Multisensory Feedback for Upper-Limb Stroke
Rehabilitation: A Controlled Study
- Authors: Xin Li, Lu Wang, Si Miao, Zan Yue, Zhiming Tang, Liujie Su, Yadan Zheng, Xiangzhen Wu, Shan Wang, Jing Wang, Zulin Dou
  Abstract: Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated the efficacy of the sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback for post-stroke rehabilitation. Furthermore, we discussed the interaction between training intensity and training duration in BCI training. Twenty-four stroke patients with severe upper limb (UL) motor deficits were randomly assigned to two groups: 2-week SMR-BCI training combined with conventional treatment (BCI Group, BG, n = 12) and 2-week conventional treatment without SMR-BCI intervention (Control Group, CG, n = 12). Motor function was measured using clinical measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Motor Functional Test (WMFT), and Modified Barthel Index (MBI), at baseline (Week 0), post-intervention (Week 2), and follow-up week (Week 4). EEG data from patients allocated to the BG was recorded at Week 0 and Week 2 and quantified by mu suppression means event-related desynchronization (ERD) in mu rhythm (8–12 Hz). All functional assessment scores (FMA-UE, WMFT, and MBI) significantly improved at Week 2 for both groups (p < 0.05). The BG had significantly higher FMA-UE and WMFT improvement at Week 4 compared to the CG. The mu suppression of bilateral hemisphere both had a positive trend with the motor function scores at Week 2. This study proposes a new effective SMR-BCI system and demonstrates that the SMR-BCI training with audio-cue, motor observation and multisensory feedback, together with conventional therapy may promote long-lasting UL motor improvement.Clinical Trial Registration: [http://www.chictr.org.cn], identifier [ChiCTR2000041119].
  PubDate: 2022-03-11T00:00:00Z