Average Power Spectral Density Research Articles

Changes in postural stability after cerebrospinal fluid tap test in patients with idiopathic normal pressure hydrocephalus.

In patients with idiopathic normal pressure hydrocephalus (iNPH), the characteristics of balance disturbance are not as well understood as those related to gait. This study examined changes in postural stability in quiet standing after the cerebrospinal fluid tap test (CSFTT) in these patients. Furthermore, the study explored the relationship between the amount of spontaneous body sway and both gait and executive function. All patients diagnosed with iNPH underwent CSFTT. We evaluated their center of pressure (COP) measurements on a force plate during quiet standing, both pre- and post-CSFTT. Following the COP measurements, we calculated COP parameters using time and frequency domain analysis and assessed changes in these parameters after CSFTT. At pre-CSFTT, we assessed the Timed Up and Go (TUG) and the Frontal Assessment Battery (FAB). We investigated the relationship between COP parameters and the TUG and FAB scores at pre-CSFTT. A total of 72 patients with iNPH were initially enrolled, and 56 patients who responded positively to CSFTT were finally included. Post-CSFTT, significant improvements were observed in COP parameters through time domain analysis. These included the velocity of COP (vCOP), root-mean-square of COP (rmsCOP), turn index, torque, and base of support (BOS), compared to the pre-CSFTT values (p < 0.05). In the frequency domain analysis of COP parameters post-CSFTT, there was a decrease in both the peak and average of power spectral density (PSD) values in both the anteroposterior (AP) and mediolateral (ML) directions below 0.5 Hz (p < 0.05). In addition, the TUG scores showed a positive correlation with vCOP, rmsCOP, turn index, torque, BOS, and both the peak and average PSD values in the AP and ML directions below 0.5 Hz (p < 0.05). The FAB scores demonstrated a negative correlation with vCOP, rmsCOP, turns index, BOS, and both peak and average PSD values in the AP direction below 0.5 Hz (p < 0.05). In patients with iNPH who responded to CSFTT, there was an improvement in spontaneous body sway during quiet standing after CSFTT. Increased spontaneous sway is associated with impaired gait and frontal lobe function. This may be linked to impaired cortico-cortical and cortico-subcortical circuits in patients with iNPH.

Velocity-pressure admittance for structural analysis of large cooling towers against strong winds

To facilitate dynamic structural analyses of large cooling towers subjected to strong winds in structural design and safety assessments, the auto-spectra of the local wind pressures on the tower shell are required. To avoid expensive and laborious physical model tests, Pirner provided empirical normalized pressure spectra for direct use, which imply that the concept of velocity-pressure admittance can be applied in the windward region on the tower shell. However, Pirner's empirical results are subjected to the fact that the dependence of pressure spectra on height or turbulence intensity of the oncoming flow is wrongfully neglected. To this end, velocity-pressure admittances are measured at various heights on a 1:200 scaled tower model in an atmospheric boundary layer wind tunnel, and unified formulae for velocity-pressure admittance on windward region of large cooling towers are fitted out for use considering the turbulence intensity effects. The unified formulae are further validated using data measured on a 167 m high full-scale cooling tower in China. The full-scale/empirical formulae comparison indicates that the unified formulae for velocity-pressure admittance proposed are applicable on the windward side [0 degrees, 70 degrees]; while a constant of 1 should be utilized as the normalized power spectral density in the circumferential region [70 degrees, 180 degrees] in structural analyses.

Noise suppression method for OPGW transmission line galloping monitoring by using adjacent FBG sensors

To solve the problem of transmission line galloping monitoring for optical power grounded waveguides (OPGWs) in external field environments, we propose a low-noise monitoring array based on adjacent sensors with low reflectivity fiber Bragg grating (FBG). We analyze the interference signal models for adjacent FBG sensors, and based on them, a noise suppression method by using adaptive filter input is constructed. Then we simulate the noise suppression effect of the proposed algorithm under different noise conditions. Finally, we deploy the low-noise OPGW transmission line galloping monitoring system based on low reflectivity FBGs in China’s western autonomous prefecture with a 220 kV transmission line. The experimental results show that, after adaptive filtering using adjacent sensors, the average noise power spectral density is reduced by 6.5 dB, and the algorithm optimizes the monitoring intensity spectrum. Oscillation events of about 100 m can be clearly observed within the monitoring interval. It creates certain conditions for further improving the typical event classification data processing and pattern recognition database for OPGWs and demonstrates promising prospects for engineering applications.

SPECTRUM OCCUPANCY ANALYSIS OF ACTIVE FM BAND IN OSOGBO METROPOLIS

An investigation was conducted to ascertain the FM band (88-108) MHz spectrum occupancy measurement within Osogbo, Nigeria over a period of 48 hours. The energy detection method was used to analyze the spectrum data, and a threshold of 5 dB/Hz above the noise level was put in place. The Fountain University Osogbo campus, the Atelewo Fire Station, the Uniosun Osogbo campus, and the National Control Centre Osogbo were the four venues for the campaign measurement of the spectrum occupancy. The fieldwork analysis led to the deduction that the Uniosun campus experienced a maximum duty cycle of 99.48% spectrum occupancy at band 9 of frequency 91.625 MHz and band 35 of frequency (104.375-104.5) MHz. Of the four locations, the Uniosun campus had the highest average duty cycle of 89.81% spectrum occupancy and the highest power spectral density of -67.50 dB/Hz. The maximum average power spectral density of -87.89 dB/Hz is recorded at the National Control Center. The Uniosun campus had the highest number of active channels, with 574 channels located at band 9 of frequency channel 91.625 MHz and band 35 of frequency channels (104.375-104.625) MHz. The university campus also had the highest average number of active channels, with 518 channels.

Alternate Electrode Placements to Facilitate Frontal Electroencephalography Monitoring in Anesthetized and Critically Ill Patients.

Frontal electroencephalography (EEG) monitoring can be useful in guiding the titration of anesthetics, but it is not always feasible to place electrodes in the standard configuration in some circumstances, including during neurosurgery. This study compares 5 alternate configurations of the Masimo Sedline Sensor. Ten stably sedated patients in the intensive care unit were recruited. Frontal EEG was monitored in the standard configuration (bifrontal upright) and 5 alternate configurations: bifrontal inverse, infraorbital, lateral upright, lateral inverse, and semilateral. Average power spectral densities (PSDs) with 95% CIs in the alternate configurations were compared to PSDs in the standard configuration. Two-one-sided-testing with Wilcoxon signed-rank tests assessed equivalence in the spectral edge frequency (SEF-95), EEG power, and relative delta (0.5 to 3.5Hz), alpha (8 to 12Hz), and beta (20 to 30Hz) power between each alternate and standard configurations. After the removal of unanalyzable tracings, 7 patients were included for analysis in the infraorbital configuration and 9 in all other configurations. In the lateral upright and lateral inverse configurations, PSDs significantly differed from the standard configuration within the 15 to 20Hz band. The greatest decrease in EEG power was in the lateral inverse configuration (median: -97dB; IQR: -130, -62dB). The largest change in frequency distribution of EEG power was in the infraorbital configuration; median SEF-95 change of -1.4Hz (IQR: -2.8, 0.7Hz), median relative delta power change of +7.3% (IQR: 1.4%, 7.9%), and median relative alpha power change of -0.6% (IQR: -5.7%, 0.0%). These 5 alternate Sedline electrode configurations are suitable options for monitoring frontal EEG when the standard configuration is not possible.

Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes.

Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis athletes and identify specific brain network biomarkers. A total of 48 healthy right-handed college students participated in this study, including 24 table tennis athletes and 24 controls with no exercise experience. Electroencephalogram data were collected using a 64-conductive active electrode system during eyes-closed resting conditions. The analysis involved examining the average power spectral density and constructing brain functional networks using the weighted phase-lag index. Network topological characteristics were then calculated. The results revealed that table tennis athletes exhibited significantly higher average power spectral density in the α band compared to the control group. Moreover, athletes not only demonstrated stronger functional connections, but they also exhibited enhanced transmission efficiency in the brain network, particularly at the local level. Additionally, a lateralization effect was observed, with more potent interconnected hubs identified in the left hemisphere of the athletes' brain. Our findings imply that the α band may be uniquely associated with table tennis athletes and their motor skills. The brain network characteristics of athletes during the resting state are worth further attention to gain a better understanding of adaptability of and changes in their brains during training and competition.

Electroencephalographic power spectrum changes in cerebral small vessel disease combined with cognitive dysfunction and its relationship with neutrophil/lymphocyte ratio and its clinical value - a pilot study.

The study aimed to explore the changes in the electrical power spectrum of the brain and its correlation with neutrophil/lymphocyte ratio (NLR) in patients with cognitively impaired cerebral small vessel disease (CSVD) and to explore its clinical application. A total of 61 patients with CSVD who attended the People's Hospital of Shaanxi Province from September 2021 to September 2022 were divided into the group with cognitive impairment (cerebral small vascular with cognitive impairment, CSVCI group, n = 29) and the group without cognitive impairment (CSVD group, n = 32) based on the Montreal Cognitive Assessment Scale (MoCA) score, while 20 healthy subjects were recruited as the control group (healthy control, HC group). EEG was performed in the three groups, and the difference in whole brain quantitative EEG power spectral density (PSD) was calculated and compared between the three groups. The PSD values in the δ and θ bands of the CSVCI group were higher than those of the CSVD group, while the PSD values in the α band were lower than those of the CSVD and HC groups. In addition, PSD values in the δ-band in the CSVD group were lower than those in the HC group (all p < 0.05). Multifactorial logistic regression showed that reduced α-band global average PSD and low years of education were independent risk factors for cognitive impairment in patients with CSVD (p < 0.05). In patients with cerebral small-vessel disease, α-band PSD was positively and δ-band PSD negatively correlated with MoCA score, and paraventricular, deep white matter, and total Fazekas scores were negatively correlated with MoCA score. Furthermore, θ-band PSD is positively correlated with NLR (all p < 0.05). EEG activity was slowed down in patients with CSVD with cognitive impairment. The α-band global mean PSD values independently affected the occurrence of cognitive impairment in CSVD patients beyond the Fazekas score. NLR may be one of the mechanisms leading to the slowing down of the EEG, which can be used as an objective indicator for the early prediction of cognitive impairment but still needs to be clarified by further studies.

Effect of heavy ion radiation on low frequency noise characteristics of AlGaN/GaN high electron mobility transistors

AlGaN/GaN high election mobility transistor (HEMT) has important application prospects in satellite communication, radar, nuclear reactors and other extreme environments, owing to its excellent electrical performance and strong radiation resistance. Heavy ion radiation mainly causes single-event effect and displacement damage effect in AlGaN/GaN HEMT device. In this work, the displacement damage defects introduced by heavy ion radiation are analyzed in detail. With the increase of heavy ion radiation influence, more defects are introduced by displacement damage. These defects reduce the two-dimensional electron gas (2DEG) concentration through carrier capture and removal effect, and reduce the carrier mobility through scattering mechanism, resulting in gradual degradation of the electrical characteristics of the device. In this work, AlGaN/GaN high electron mobility transistors are irradiated by &lt;sup&gt;181&lt;/sup&gt;Ta&lt;sup&gt;32+&lt;/sup&gt; ions with fluences of 1×10&lt;sup&gt;8&lt;/sup&gt; ions/cm&lt;sup&gt;2&lt;/sup&gt;, 1×10&lt;sup&gt;9&lt;/sup&gt; ions/cm&lt;sup&gt;2&lt;/sup&gt; and 1×10&lt;sup&gt;10&lt;/sup&gt; ions/cm&lt;sup&gt;2&lt;/sup&gt;. The electrical characteristics, EMMI and low-frequency noise characteristics of the device before and after heavy ion radiation are measured. The results show that heavy ion radiation can lead to the degradation of electrical parameters. When the heavy ion radiation dose reaches 1×10&lt;sup&gt;10&lt;/sup&gt; ions/cm&lt;sup&gt;2&lt;/sup&gt;, the electrical characteristics of the device deteriorate seriously, the threshold voltage shifts forward by 25%, and the drain saturation current deteriorates obviously. The defect locations introduced by irradiation are analyzed by EMMI test, and it is found that the number of “hot spots” increases significantly after the having been irradiated by heavy ions with a fluence of 1×10&lt;sup&gt;10&lt;/sup&gt; ions/cm&lt;sup&gt;2&lt;/sup&gt;, indicating that the radiation leads to the increase of defect density and serious damage to the device. Through the noise test, it is found that with the increase of the radiation fluence, the current noise power spectral density gradually increases. When the fluence reaches 1×10&lt;sup&gt;10&lt;/sup&gt; ions/cm&lt;sup&gt;2&lt;/sup&gt;, the defect density increases to 3.19×10&lt;sup&gt;18&lt;/sup&gt; cm&lt;sup&gt;–3&lt;/sup&gt;·eV&lt;sup&gt;–1&lt;/sup&gt;, and the Hooge parameter increases after having been irradiated by heavy ions. We believe that the radiation leads to the defect density and parasitic series resistance of AlGaN/GaN HEMT device to increases, resulting in larger Hooge parameters. Through analyzing the variation of the normalized power spectral density of the drain current noise with bias voltage, it is found that the defects caused by heavy ion radiation will cause the parasitic series resistance to increase.

Quantifying unsupported sitting posture impairments in humans with cervical spinal cord injury using a head-mounted IMU sensor.

Cross-sectional study. To evaluate unsupported sitting posture impairments and identify postural regulatory strategies in cervical spinal cord injury (cSCI) participants via a head-mounted IMU sensor. A research lab in the United States of America. cSCI participants and controls maintained postural stability during unsupported sitting with eyes either open or closed. The head-mounted IMU sensor recorded accelerometer data to calculate cumulative sway motion. The postural regulatory strategy was analyzed by assessing the normalized power spectral density (PSD) in four frequency bands: 0-0.1 Hz (visual regulation), 0.1-0.5 Hz (vestibular regulation), 0.5-1 Hz (cerebellar regulation), and >1 Hz (proprioception and muscle control). Significant increases in postural sway were observed in cSCI participants compared to controls during unsupported sitting. For cSCI participants, normalized PSD significantly increased in the low-frequency bands (0-0.1 Hz and 0.1-0.5 Hz) but decreased in the high-frequency band (>1 Hz) compared to controls. cSCI participants were more reliant on visual and vestibular systems for sitting balance, while depending less on proprioception and muscle control compared to controls. These findings suggest that the altered postural regulatory strategy is ineffective in maintaining postural stability during unsupported sitting, emphasizing the importance of proprioception and muscle control for seated postural stability in cSCI participants.

Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks

Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications.

Investigation of the acoustic properties of underwater rainfall noise measured by a bottom-mounted hydrophone and its application to deep learning to classify and estimate rainfall

Although the ocean covers more than 70 % of the Earth's surface, it is difficult to measure precipitation over the ocean due to the technical limitations of platforms such as surface buoys and satellites. Recently, the estimation of precipitation has been tried using the underwater sound generated by rainfall. In this talk, we present the results of comparing underwater noise and rainfall with and without precipitation using the underwater noise and rainfall data provided by OOI (Ocean Observatories Initiative, USA). The acoustic data was acquired by a bottom-moored hydrophone at a depth of 80 m off the coast of Oregon, and the rainfall data was measured from a rain gauge installed on a surface buoy. The 1-minute averaged power spectral densities for ambient noise were computed for 26 days from 5 - 30 April 2018, which were compared with rain rate and wind speed. Based on the comparison results, the presence or absence of rainfall was determined through a pattern recognition neural network, and finally, the amount of precipitation was estimated through multiple linear regression analysis.

Chronic subdural electrocorticography in nonhuman primates by an implantable wireless device for brain-machine interfaces.

Subdural electrocorticography (ECoG) signals have been proposed as a stable, good-quality source for brain-machine interfaces (BMIs), with a higher spatial and temporal resolution than electroencephalography (EEG). However, long-term implantation may lead to chronic inflammatory reactions and connective tissue encapsulation, resulting in a decline in signal recording quality. However, no study has reported the effects of the surrounding tissue on signal recording and device functionality thus far. In this study, we implanted a wireless recording device with a customized 32-electrode-ECoG array subdurally in two nonhuman primates for 15 months. We evaluated the neural activities recorded from and wirelessly transmitted to the devices and the chronic tissue reactions around the electrodes. In addition, we measured the gain factor of the newly formed ventral fibrous tissue in vivo. Time-frequency analyses of the acute and chronic phases showed similar signal features. The average root mean square voltage and power spectral density showed relatively stable signal quality after chronic implantation. Histological examination revealed thickening of the reactive tissue around the electrode array; however, no evident inflammation in the cortex. From gain factor analysis, we found that tissue proliferation under electrodes reduced the amplitude power of signals. This study suggests that subdural ECoG may provide chronic signal recordings for future clinical applications and neuroscience research. This study also highlights the need to reduce proliferation of reactive tissue ventral to the electrodes to enhance long-term stability.

A Deep Neural Network for Working Memory Load Prediction from EEG Ensemble Empirical Mode Decomposition

Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early detection of WM dysfunction is essential to predict the onset of MCI and AD. Artificial Intelligence (AI)-based algorithms are increasingly used to identify biomarkers for detecting subtle changes in loaded WM. This paper presents an approach using electroencephalograms (EEG), time-frequency signal processing, and a Deep Neural Network (DNN) to predict WM load in normal and MCI-diagnosed subjects. EEG signals were recorded using an EEG cap during working memory tasks, including block tapping and N-back visuospatial interfaces. The data were bandpass-filtered, and independent components analysis was used to select the best electrode channels. The Ensemble Empirical Mode Decomposition (EEMD) algorithm was then applied to the EEG signals to obtain the time-frequency Intrinsic Mode Functions (IMFs). The EEMD and DNN methods perform better than traditional machine learning methods as well as Convolutional Neural Networks (CNN) for the prediction of WM load. Prediction accuracies were consistently higher for both normal and MCI subjects, averaging 97.62%. The average Kappa score for normal subjects was 94.98% and 92.49% for subjects with MCI. Subjects with MCI showed higher values for beta and alpha oscillations in the frontal region than normal subjects. The average power spectral density of the IMFs showed that the IMFs (p = 0.0469 for normal subjects and p = 0.0145 for subjects with MCI) are robust and reliable features for WM load prediction.

Efficient ground motion conditional simulation method for nonstationary multivariate processes based on Kriging method and orthogonal discrete wavelet transform

Conditional ground motion simulation methods preserve more physical features of actual ground motions than unconditional methods by including actual earthquake records. This study developed a conditional multivariate nonstationary process simulation method based on the combination of the Kriging method and the orthogonal discrete wavelet transform (DWT). The Kriging method is used to ensure the spectral coherences between simulations as prescribed and is extended to nonstationary random processes by applying it to the average power spectral density (APSD) of ground motions. The ground motions' DWT wavelet coefficients are assumed to be uniformly time-modulated stationary processes whose time-modulating functions are the envelopes of the seed record's wavelet coefficients. The APSD and the envelopes together outline the energy distribution of the random field in both the time and frequency domain features. The power spectral densities of the stationary random processes' DWT coefficients are derived and used to estimate the APSD of the ground motions' DWT coefficients. Approaches to realizing uniformly time-modulated stationary processes with prescribed APSDs and envelope functions are developed. Monte Carlo simulations were conducted to verify the new method's effectiveness. Results show that both the target APSD and coherences can be well approximated, but precise approximation cannot always be realized in the high-frequency range. The new approach possesses a simple structure, involves simple algorithms, and applies to time-independent spectral coherence models, making it an efficient method compared with existing methods.

Speech Segmentation Algorithm Based on an Analysis of the Normalized Power Spectral Density

This article demonstrates a new approach to speaker independent phoneme detection. The core of the algorithm is to measure the distance between normalized power spectral densities in adjacent, short-time segments and verify it based on velocity of changes of values of short-time signal energy analysis. The results of experiment analysis indicate that proposed algorithm allows revealing a phoneme structure of pronounced speech with high probability. The advantages of this algorithm are absence of any prior information on a signal or model of phonemes and speakers that allows the algorithm to be speaker independent and have a low computation complexity.

Low-frequency noise in quasi-ballistic monolithic Al–Ge–Al nanowire field effect transistors

In this work, Al2O3-passivated, monolithic, and crystalline Al–Ge–Al heterostructure nanowire field effect transistors (FETs) with Ge channel lengths ranging from 18 to 826 nm are analyzed from a low-frequency noise perspective. 1/f and random telegraph noise (RTN) are analyzed in an accumulation mode, where the hole channel is formed by applying a back-gate potential VG. The normalized power spectral density of drain current fluctuations of 1/f noise (SID/ID2) at medium currents follows nearly an 1/ID trend. 1/f noise is analyzed within both the mobility and carrier number fluctuation models (MFM and CNFM), respectively. Taking the MFM into account, the Hooge noise parameter α spreads in the interval of 1.5 × 10−4 to 4 × 10−2, with lower values for shorter devices. Using the same data and the CNFM, the density of interface states Dit in the Al2O3/GexOy/Ge system was estimated using the transconductance extracted from the quasi-static transfer I/V characteristics. The extracted Dit values range from 5 × 109 to 3 × 1012 cm−2 eV−1. Contact noise has also been observed in some devices at high currents. RTN analyzed in time domain exhibits a relative RTN amplitude in the 0.3%–20% range. Capture and emission time constants as a function of VG exhibit a typical behavior for metal oxide semiconductor FETs. The extracted noise parameters are comparable with Ge and III–V nanodevices of top-down and bottom-up technologies.

Data-Driven Method for Porosity Measurement of Thermal Barrier Coatings Using Terahertz Time-Domain Spectroscopy

Accurate measurement of porosity is crucial for comprehensive performance evaluation of thermal barrier coatings (TBCs) on aero-engine blades. In this study, a novel data-driven predictive method based on terahertz time-domain spectroscopy (THz-TDS) was proposed. By processing and extracting features from terahertz signals, multivariate parameters were composed to characterize the porosity. Principal component analysis, which enabled effective representation of the complex signal information, was introduced to downscale the dimensionality of the time-domain data. Additionally, the average power spectral density of the frequency spectrum and the extreme points of the first-order derivative of the phase spectrum were extracted. These extracted parameters collectively form a comprehensive set of multivariate parameters that accurately characterize porosity. Subsequently, the multivariate parameters were used as inputs to construct an extreme learning machine (ELM) model optimized by the sparrow search algorithm (SSA) for predicting porosity. Based on the experimental results, it was evident that the predictive accuracy of SSA-ELM was significantly higher than the basic ELM. Furthermore, the robustness of the model was evaluated through K-fold cross-validation and the final model regression coefficient was 0.92, which indicates excellent predictive performance of the data-driven model. By introducing the use of THz-TDS and employing advanced signal processing techniques, the data-driven model provided a novel and effective solution for the rapid and accurate detection of porosity in TBCs. The findings of this study offer valuable references for researchers and practitioners in the field of TBCs inspection, opening up new avenues for improving the overall assessment and performance evaluation of these coatings.

Anodal transcranial direct current stimulation enhances ankle force control and modulates the beta-band activity of the sensorimotor cortex.

This study aimed to investigate the cortical responses to the ankle force control and the mechanism underlying changes in ankle force control task induced by transcranial direct current stimulation (tDCS). Sixteen young adults were recruited, and they completed the electroencephalogram (EEG) assessment and high-definition tDCS (HD-tDCS) sessions. Root mean square (RMS) error was used to evaluate ankle force control task performance. Spectral power analysis was conducted to extract the average power spectral density (PSD) in the alpha (8-13Hz) and beta (13-30Hz) bands for resting state and tasking (i.e. task-PSD). The ankle force control task induced significant decreases in alpha and beta PSDs in the central, left, and right primary sensorimotor cortex (SM1) and beta PSD in the central frontal as compared with the resting state. HD-tDCS significantly decreased the RMS and beta task-PSD in the central frontal and SM1. A significant association between the percent change of RMS and the percent change of beta task-PSD in the central SM1 after HD-tDCS was observed. In conclusion, ankle force control task activated a distributed cortical network mainly including the SM1. HD-tDCS applied over SM1 could enhance ankle force control and modulate the beta-band activity of the sensorimotor cortex.

The Reaching Phase of Feeding and Self-Care Actions Optimizes Action Observation Effects in Chronic Stroke Subjects

Background The Action Observation Therapy (AOT) is a well-established post-stroke rehabilitation treatment based on the theoretical framework of the Mirror Neuron System (MNS) activation. However, AOT protocols are still heterogeneous in terms of video contents of observed actions. Objective The aim of this study was to analyze electroencephalographic (EEG) recordings in stroke patients during the observation of different videos of task-specific upper limb movements, and to define which category of actions can elicit a stronger cortical activation in the observer’s brain. Methods Signals were analyzed from 19 chronic stroke subjects observing customized videos that represented 3 different categories of upper limb actions: Finalized Actions, Non-Finalized Actions, and Control Videos. The Event-Related Desynchronization in the µ and β bands was chosen to identify the involvement of the cerebral cortex: the area of the normalized power spectral density was calculated for each category and, deepening, for the reaching and completion sub-phases of Finalized Actions. For descriptive purposes, the time course of averaged signal power was described. The Kruskal–Wallis test (P < .05) was applied. Results The analysis showed a greater desynchronization when subjects observed Finalized Actions with respect to Non-Finalized in all recorded areas; Control videos provoked a synchronization in the same areas and frequency bands. The reaching phase of feeding and self-care actions evoked a greater suppression both in µ and β bands. Conclusions The observation of finalized arm movements seems to elicit the strongest activation of the MNS in chronic stroke patients. This finding may help the clinicians to design future AOT-based stroke rehabilitation protocols. Clinical Trial Registration: Clinical Trial Registration—URL: http://www.clinicaltrials.gov. Unique identifier: NCT04047134.

Identifying oscillations under multi-site sensory stimulation for high-level peripheral nerve injured patients: a pilot study

Objective. For high-level peripheral nerve injuryed (PNI) patients with severe sensory dysfunction of upper extremities, identifying the multi-site tactile stimulation is of great importance to provide neurorehabilitation with sensory feedback. In this pilot study, we showed the feasibility of identifying multi-site and multi-intensity tactile stimulation in terms of electroencephalography (EEG). Approach. Three high-level PNI patients and eight non-PNI participants were recruited in this study. Four different sites over the upper arm, forearm, thumb finger and little finger were randomly stimulated at two intensities (both sensory-level) based on the transcutaneous electrical nerve stimulation. Meanwhile, 64-channel EEG signals were recorded during the passive tactile sense stimulation on each side. Main results. The spatial-spectral distribution of brain oscillations underlying multi-site sensory stimulation showed dominant power attenuation over the somatosensory and prefrontal cortices in both alpha-band (8–12 Hz) and beta-band (13–30 Hz). But there was no significant difference among different stimulation sites in terms of the averaged power spectral density over the region of interest. By further identifying different stimulation sites using temporal-spectral features, we found the classification accuracies were all above 89% for the affected arm of PNI patients, comparable to that from their intact side and that from the non-PNI group. When the stimulation site-intensity combinations were treated as eight separate classes, the classification accuracies were ranging from 88.89% to 99.30% for the affected side of PNI subjects, similar to that from their non-affected side and that from the non-PNI group. Other performance metrics, including specificity, precision, and F1-score, also showed a sound identification performance for both PNI patients and non-PNI subjects. Significance. These results suggest that reliable brain oscillations could be evoked and identified well, even though induced tactile sense could not be discerned by the PNI patients. This study have implication for facilitating bidirectional neurorehabilitation systems with sensory feedback.