scholarly journals Electrophysiological Correlates of Virtual-Reality Applications in the Rehabilitation Setting: New Perspectives for Stroke Patients

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 836
Author(s):  
Francesco Arcuri ◽  
Camillo Porcaro ◽  
Irene Ciancarelli ◽  
Paolo Tonin ◽  
Antonio Cerasa
Keyword(s):  
Virtual Reality ◽  
Brain Plasticity ◽  
Eeg Signal ◽  
Stroke Patients ◽  
Time Frequency ◽  
Non Invasive ◽  
Power Spectral ◽  
Frequency Domains ◽  
Multisensory Training ◽  
Vr Training

Here we reviewed the last evidence on the application of electroencephalography (EEG) as a non-invasive and portable neuroimaging method useful to extract hallmarks of neuroplasticity induced by virtual reality (VR) rehabilitation approaches in stroke patients. In the neurorehabilitation context, VR training has been used extensively to hamper the effects of motor treatments on the stroke’s brain. The concept underlying VR therapy is to improve brain plasticity by engaging users in multisensory training. In this narrative review, we present the key concepts of VR protocols applied to the rehabilitation of stroke patients and critically discuss challenges of EEG signal when applied as endophenotype to extract neurophysiological markers. When VR technology was applied to magnify the effects of treatments on motor recovery, significant EEG-related neural improvements were detected in the primary motor circuit either in terms of power spectral density or as time-frequency domains.

scholarly journals Neurorehabilitation of Spatial Memory Using Virtual Environments: A Systematic Review

2019 ◽  
Vol 8 (10) ◽  
pp. 1516 ◽  
Author(s):  
Montana ◽  
Tuena ◽  
Serino ◽  
Cipresso ◽  
Riva
Keyword(s):  
Virtual Reality ◽  
Spatial Memory ◽  
Virtual Environments ◽  
Cognitive Deficits ◽  
Brain Plasticity ◽  
Assessment Tools ◽  
Clinical Settings ◽  
Vr Training ◽  
Current Scenario ◽  
Selection Of

In recent years, virtual reality (VR) technologies have become widely used in clinical settings because they offer impressive opportunities for neurorehabilitation of different cognitive deficits. Specifically, virtual environments (VEs) have ideal characteristics for navigational training aimed at rehabilitating spatial memory. A systematic search, following PRISMA guidelines, was carried out to explore the current scenario in neurorehabilitation of spatial memory using virtual reality. The literature on this topic was queried, 5048 papers were screened, and 16 studies were included, covering patients presenting different neuropsychological diseases. Our findings highlight the potential of the navigational task in virtual environments (VEs) for enhancing navigation and orientation abilities in patients with spatial memory disorders. The results are promising and suggest that VR training can facilitate neurorehabilitation, promoting brain plasticity processes. An overview of how VR-based training has been implemented is crucial for using these tools in clinical settings. Hence, in the current manuscript, we have critically debated the structure and the length of training protocols, as well as a different type of exploration through VR devices with different degrees of immersion. Furthermore, we analyzed and highlighted the crucial role played by the selection of the assessment tools.

scholarly journals Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Amjed S. Al-Fahoum ◽  
Ausilah A. Al-Fraihat
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Eeg Signal ◽  
Property A ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Signal Features ◽  
Frequency Domains ◽  
Auto Regressive ◽  
The Cost

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

PREDICTING CLINICAL RESPONSE TO TRANSCRANIAL MAGNETIC STIMULATION IN MAJOR DEPRESSION USING TIME-FREQUENCY EEG SIGNAL PROCESSING

2021 ◽  
pp. 2150048
Author(s):  
Elias Ebrahimzadeh ◽  
Mostafa Asgarinejad ◽  
Sarah Saliminia ◽  
Sarvenaz Ashoori ◽  
Masoud Seraji
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Major Depression ◽  
Clinical Outcomes ◽  
Magnetic Stimulation ◽  
Brain Plasticity ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Support Vector ◽  
Eeg Signal ◽  
Beta Band ◽  
Time Frequency

Repetitive transcranial magnetic stimulation (rTMS) is defined as a noninvasive technique of brain stimulation conducted for both diagnostic and therapeutic purposes. rTMS can effectively excite the brain neurons and increase brain plasticity, which becomes particularly useful in psychiatric and neurological fields. Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a noninvasive neurophysiological test that is promising as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel nonlinear index of the resting state EEG activity as a predictor of clinical outcome and compare its predictive capacity to traditional frequency-based indices. EEG was recorded from 50 patients with treatment resistant depression (TRD) and 24 healthy comparison (HC) subjects. TRD patients were treated with excitatory rTMS to the dorsolateral prefrontal cortex (DLPFC) for 4–6 weeks. EEG signals were first decomposed using the ICA algorithm and the extracted components were then processed by time-frequency analysis. We then go on to compare the participants’ depression severity before, after, and 2 months after finishing the last treatment session using the proposed rTMS therapy. Absolute powers (APs), band powers (BPs), and theta and beta band entropies (BAs), which were extracted from the EEG, are used as features for the classification of changes in patients and normal cases after applying rTMS. Accordingly, we can go beyond the Beck score and clinically classify the EEG signal into two classes: depression and normal. The results demonstrated 78.37%, 74.32%, and 82.43% accuracy for artificial neural network (ANN), [Formula: see text]-nearest neighbor (KNN), and support vector machine (SVM) classifiers, respectively, indicating the superiority of the proposed method to those mentioned in similar studies. Also, the electrophysiological changes are shown to be evident in patients with major depression. Our data show that the time-frequency index yields superior outcome prediction performance compared to the traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression.

Cognitive State Estimation by Effective Feature Extraction and Proper Channel Selection of EEG Signal

2014 ◽  
Vol 24 (02) ◽  
pp. 1540005 ◽  
Author(s):  
Monira Islam ◽  
Tazrin Ahmed ◽  
Md. Salah Uddin Yusuf ◽  
Mohiuddin Ahmad
Keyword(s):  
Feature Extraction ◽  
Cognitive State ◽  
Eeg Signal ◽  
Classification Rate ◽  
Time Frequency ◽  
Cognitive States ◽  
Alpha Channel ◽  
Power Spectral ◽  
Eeg Data ◽  
Selection Of

This paper presents a cognitive state estimation system focused on some effective feature extraction based on temporal and spectral analysis of electroencephalogram (EEG) signal and the proper channel selection of the BIOPAC automated EEG analysis system. In the proposed approach, different frequency components (i) real value; (ii) imaginary value; (iii) magnitude; (iv) phase angle and (v) power spectral density of the EEG data samples during different mental task performed to assess seven types of human cognitive states — relax, mental task, memory related task, motor action, pleasant, fear and enjoying music on the three channels of BIOPAC EEG data acquisition system — EEG, Alpha and Alpha RMS signal. Also the time and time-frequency-based features were extracted to compare the performance of the system. After feature extraction, the channel efficacy is evaluated by support vector machine (SVM) based on the classification rate in different cognitive states. From the experimental results and classification accuracy, it is determined that the overall accuracy for alpha channel shows much improved result for power spectral density than the other frequency based features and other channels. The classification rate is 69.17% for alpha channel whereas for EEG and alpha RMS channel it is found 47.22% and 32.21%, respectively. For statistical analysis standard deviation shows better result for alpha channel and it is found 65.4%. The time-frequency analysis shows much improved result for alpha channel also. For the mean value of DWT coefficients the accuracy is highest and it is 81.3%. Besides the classification accuracy, SVM shows better performance in compare with kNN classifier.

scholarly journals A Structured Approach to Test the Signal Quality of Electroencephalography Measurements During Use of Head-Mounted Displays for Virtual Reality Applications

2021 ◽  
Vol 15 ◽  
Author(s):  
Desirée Weber ◽  
Stephan Hertweck ◽  
Hisham Alwanni ◽  
Lukas D. J. Fiederer ◽  
Xi Wang ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Brain Activity ◽  
Signal Quality ◽  
Eeg Signal ◽  
Frequency Range ◽  
Non Invasive ◽  
Head Mounted Displays ◽  
Sensory Evoked ◽  
Structured Approach

Joint applications of virtual reality (VR) systems and electroencephalography (EEG) offer numerous new possibilities ranging from behavioral science to therapy. VR systems allow for highly controlled experimental environments, while EEG offers a non-invasive window to brain activity with a millisecond-ranged temporal resolution. However, EEG measurements are highly susceptible to electromagnetic (EM) noise and the influence of EM noise of head-mounted-displays (HMDs) on EEG signal quality has not been conclusively investigated. In this paper, we propose a structured approach to test HMDs for EM noise potentially harmful to EEG measures. The approach verifies the impact of HMDs on the frequency- and time-domain of the EEG signal recorded in healthy subjects. The verification task includes a comparison of conditions with and without an HMD during (i) an eyes-open vs. eyes-closed task, and (ii) with respect to the sensory- evoked brain activity. The approach is developed and tested to derive potential effects of two commercial HMDs, the Oculus Rift and the HTC Vive Pro, on the quality of 64-channel EEG measurements. The results show that the HMDs consistently introduce artifacts, especially at the line hum of 50 Hz and the HMD refresh rate of 90 Hz, respectively, and their harmonics. The frequency range that is typically most important in non-invasive EEG research and applications (<50 Hz) however, remained largely unaffected. Hence, our findings demonstrate that high-quality EEG recordings, at least in the frequency range up to 50 Hz, can be obtained with the two tested HMDs. However, the number of commercially available HMDs is constantly rising. We strongly suggest to thoroughly test such devices upfront since each HMD will most likely have its own EM footprint and this article provides a structured approach to implement such tests with arbitrary devices.

scholarly journals Evolutionary Spectra Estimation of Field Measurement Typhoon Processes Using Wavelets

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Guang-Dong Zhou ◽  
You-Liang Ding ◽  
Ai-Qun Li
Keyword(s):  
Estimation Method ◽  
Engineering Application ◽  
Frequency Component ◽  
Time Frequency ◽  
Whole Process ◽  
Power Spectral ◽  
Frequency Domains ◽  
Definition Of ◽  
Evolutionary Spectra ◽  
Oscillatory Processes

This paper presents a wavelet-based method for estimating evolutionary power spectral density (EPSD) of nonstationary stochastic oscillatory processes and its application to field measured typhoon processes. The EPSD, which is deduced in a closed form based on the definition of the EPSD and the algorithm of the continuous wavelet transform, can be formulated as a sum of squared moduli of the wavelet functions in time domain modulated by frequency-dependent coefficients that relate to the squared values of wavelet coefficients and two wavelet functions with different time shifts. A parametric study is conducted to examine the efficacy of the wavelet-based estimation method and the accuracy of different wavelets. The results indicate that all of the estimated EPSDs have acceptable accuracy in engineering application and the Morlet transform can provide desirable estimations in both time and frequency domains. Finally, the proposed method is adopted to investigate the time-frequency characteristics of the Typhoon Matsa measured in bridge site. The nonstationary energy distribution and stationary frequency component during the whole process are found. The work in this paper may promote an improved understanding of the nonstationary features of typhoon winds.

scholarly journals Effects of virtual reality training on upper limb function, balance, and walking ability in stroke patients: a systematic review and meta-meta analysis (Preprint)

2021 ◽  
Author(s):  
Jinlong Wu ◽  
Aihua Zeng ◽  
Ziyan Chen ◽  
Ye Wei ◽  
Kunlun Huang ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Upper Limb ◽  
Meta Analysis ◽  
Walking Ability ◽  
Stroke Patients ◽  
High Quality ◽  
Limb Function ◽  
Upper Limb Function ◽  
Vr Training ◽  
The Impact

BACKGROUND Virtual reality (VR) training is a promising intervention strategy, which has been utilized in healthcare fields like stroke rehabilitation and psychotherapy. The current study suggests that, VR training is effective in improving the locomotor ability of individuals with stroke patients. OBJECTIVE This is the first meta-meta-analysis of the effects of virtual reality on motor function in stroke patients. This study aimed to systematically summarize and quantify the present meta-analyses results of VR training, and produce high-quality meta-meta-analysis results to obtain a more accurate prediction. METHODS We searched four online databases (Web of Science, Scopus, PubMed, and Chinese National Knowledge Infrastructure) for the meta-analysis studies. After accounting for the overlap, ten studies (almost 550 stroke patients) were obtained. Based on the meta-meta-analysis of these patients, this study quantified the impact of VR training on stroke patients’ motor performance, mainly including upper limb function, balance, and walking ability. We combined the effects under the random effect model and pooled the estimates as standardized mean differences (SMD). RESULTS The results of the meta-meta analysis showed that VR intervention effectively improved the upper limb function (SMD= 4.606, 95% confidence interval (Cl): 2.733-6.479, P< 0.05) and balance (SMD=2.101, 95%Cl：0.202-4.000, P< 0.05) of stroke patients. However, the results showed considerable heterogeneity, and thus, may need to be treated with caution. Due to the limited research, the meta-meta-analysis of walking ability was not performed. CONCLUSIONS These findings represent a comprehensive body of high-quality evidence that, VR interventions were more effective at improving the upper limb function and balance of stroke patients.

scholarly journals The Effects of Virtual Reality Training on Function in Chronic Stroke Patients: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Han Suk Lee ◽  
Yoo Junk Park ◽  
Sun Wook Park
Keyword(s):  
Virtual Reality ◽  
Upper Limb ◽  
Effect Size ◽  
Lower Limb ◽  
Meta Analysis ◽  
Chronic Stroke ◽  
Stroke Patients ◽  
Limb Function ◽  
Vr Training ◽  
Lower Limb Function

Objective. The aim of this study was to perform a meta-analysis to examine whether virtual reality (VR) training is effective for lower limb function as well as upper limb and overall function in chronic stroke patients. Methods. Three databases, OVID, PubMed, and EMBASE, were used to collect articles. The search terms used were “cerebrovascular accident (CVA),” “stroke”, and “virtual reality”. Consequently, twenty-one studies were selected in the second screening of meta-analyses. The PEDro scale was used to assess the quality of the selected studies. Results. The total effect size for VR rehabilitation programs was 0.440. The effect size for upper limb function was 0.431, for lower limb function it was 0.424, and for overall function it was 0.545. The effects of VR programs on specific outcomes were most effective for improving muscle tension, followed by muscle strength, activities of daily living (ADL), joint range of motion, gait, balance, and kinematics. Conclusion. The VR training was effective in improving the function in chronic stroke patients, corresponding to a moderate effect size. Moreover, VR training showed a similar effect for improving lower limb function as it did for upper limb function.

scholarly journals Recurrence plot structures reflect motor-related EEG pattern

2019 ◽  
pp. 282-286
Author(s):  
Elena Pitsik ◽  
Nikita Frolov
Keyword(s):  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Recurrence Plot ◽  
Mathematical Tool ◽  
Recurrence Plots ◽  
Time Frequency ◽  
Non Invasive ◽  
Power Spectral ◽  
Nonstationary Data ◽  
Eeg Recordings

Detection and classification of motor-related brain patterns from non-invasive electroencephalograms (EEGs) is challenging due to their non-stationarity and low signal-to-noise ratio and requires using advanced mathematical approaches. Traditionally applied methods such as time-frequency analysis and spatial filtering allow to quantify the main attribute of the motor-related brain activity – contralateral desynchronization of mu-band oscillations (8-13 Hz) in sensorimotor cortex – by measuring EEG signal’s amplitude, power spectral density, location etc. However, these features suffer from strong inter- and intra-subject variability. So, special attention is paid to the finding of stable features. In present paper, we investigate application of the recurrence plots – robust mathematical tool for nonstationary data analysis – to explore properties of motor-related EEG samples. Our goal is to show that recurrence plots are sensitive to the changes in brain activity accessed from noninvasive EEG recordings and may provide us a new context for interpretation of motor-related pattern in EEG.

scholarly journals Performance of channel selection used for Multi-class EEG signal classification of motor imagery

2019 ◽  
Vol 15 (3) ◽  
pp. 1305
Author(s):  
Djelloul Kheira ◽  
M. Beladgham
Keyword(s):  
Support Vector ◽  
Eeg Signal ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Left Hand ◽  
Time Frequency ◽  
Non Invasive ◽  
Average Accuracy ◽  
Motor Activities

<p>In this paper, a study of a non-invasive brain-machine interfaces for the classification of 4 imaginary are presented. Performance comparisons using time-frequency analysis between the Linear Discriminant Analysis motor activities (left hand, right hand, foot, tongue) with the BCI competition III dataset IIIa is (LDA), the Support Vector Machine (SVM) and the K-Nearest Neighbors (KNN) algorithms have been carried. The number and position of electrodes for each subject were investigated to provide an improvement for the classification accuracy of the algorithm. Results show that the electrode positions varied from subject to subject; moreover , using one subset of the channels enhanced the classification performances compared to literature data. an average accuracy of 86.06% was observed among all 3 subjects.<strong></strong></p>

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