ETucker: a constrained tensor decomposition for single trial ERP extraction.

Objective.In this paper, we propose a new tensor decomposition to extract event-related potentials (ERP) by adding a physiologically meaningful constraint to the Tucker decomposition.Approach.We analyze the performance of the proposed model and compare it with Tucker decomposition by synthesizing a dataset. The simulated dataset is generated using a 12th-order autoregressive model in combination with independent component analysis (ICA) on real no-task electroencephalogram (EEG) recordings. The dataset is manipulated to contain the P300 ERP component and to cover different SNR conditions, ranging from 0 to -30 dB, to simulate the presence of the P300 component in extremely noisy recordings. Furthermore, in order to assess the practicality of the proposed methodology in real-world scenarios, we utilized the brain-computer interface (BCI) competition III-dataset II.Main results.Our primary results demonstrate the superior performance of our approach compared to conventional methods commonly employed for single-trial estimation. Additionally, our method outperformed both Tucker decomposition and non-negative Tucker decomposition in the synthesized dataset. Furthermore, the results obtained from real-world data exhibited meaningful performance and provided insightful interpretations for the extracted P300 component.Significance.The findings suggest that the proposed decomposition is eminently capable of extracting the target P300 component's waveform, including latency and amplitude as well as its spatial location, using single-trial EEG recordings.

Tractable Maximum Likelihood Estimation for Latent Structure Influence Models With Applications to EEG & ECoG Processing.

Brain signals are nonlinear and nonstationary time series, which provide information about spatiotemporal patterns of electrical activity in the brain. CHMMs are suitable tools for modeling multi-channel time-series dependent on both time and space, but state-space parameters grow exponentially with the number of channels. To cope with this limitation, we consider the influence model as the interaction of hidden Markov chains called Latent Structure Influence Models (LSIMs). LSIMs are capable of detecting nonlinearity and nonstationarity, making them well suited for multi-channel brain signals. We apply LSIMs to capture the spatial and temporal dynamics in multi-channel EEG/ECoG signals. The current manuscript extends the scope of the re-estimation algorithm from HMMs to LSIMs. We prove that the re-estimation algorithm of LSIMs will converge to stationary points corresponding to Kullback-Leibler divergence. We prove convergence by developing a new auxiliary function using the influence model and a mixture of strictly log-concave or elliptically symmetric densities. The theories that support this proof are derived from previous studies by Baum, Liporace, Dempster, and Juang. We then develop a closed-form expression for re-estimation formulas using tractable marginal forward-backward parameters defined in our previous study. Simulated datasets and EEG/ECoG recordings confirm the practical convergence of the derived re-estimation formulas. We also study the use of LSIMs for modeling and classification on simulated and real EEG/ECoG datasets. Based on AIC and BIC, LSIMs perform better than HMMs and CHMMs in modeling embedded Lorenz systems and ECoG recordings. LSIMs are more reliable and better classifiers than HMMs, SVMs and CHMMs in 2-class simulated CHMMs. EEG biometric verification results indicate that the LSIM-based method improves the area under curve (AUC) values by about 6.8% and decreases the standard deviation of AUC values from 5.4% to 3.3% compared to the existing HMM-based method for all conditions on the BED dataset.

Effects of Soybean and Linseed Oils Calcium Salts and Starter Protein Content on Growth Performance, Immune Response, and Nitrogen Utilization Efficiency in Holstein Dairy Calves.

This study aimed to investigate the interaction of fatty acid (FA) source [calcium salt of soybean oil (n-6 FA) vs. calcium salt of linseed oil (n-3 FA) both 3% DM basis] with protein content (18% vs. 22% CP, based on DM) on growth performance, blood metabolites, immune function, skeletal growth indices, urinary purine derivatives (PD), and microbial protein synthesis (MPS) in young dairy calves. Forty 3-day-old calves (20 females and 20 males) with a starting body weight (BW) of 40.2 kg were assigned in a completely randomized block design in a 2 × 2 factorial arrangement of treatments. Experimental diets were: (1) n-6 FA with 18% CP (n-6-18CP), (2) n-6 FA with 22% CP (n-6-22CP), (3) n-3 FA with 18% CP (n-3-18CP), and (4) n-3 FA with 22% CP (n-3-22CP). Starter feed intake and average daily gain (ADG) were not influenced by experimental diets (p > 0.05). However, before weaning and the entire period, feed efficiency (FE) was greater in calves fed n-3 FA compared to n-6 FA (p < 0.05). Heart girth (weaning, p < 0.05) and hip height (weaning, p < 0.05 and final, p < 0.01) were highest among experimental treatments in calves who received n-3-22CP diets. The greatest blood glucose (p < 0.05) and insulin (p < 0.01) concentrations in the pre-weaning period and the lowest serum concentration of tumor necrosis factor (before weaning, p < 0.05) were observed in calves fed the n-3-22CP diet. However, the greatest blood urea N (before weaning, p < 0.05; after weaning, p < 0.05) and urinary N excretion (p < 0.05) were found in calves fed n-6-22CP diets compared to other experimental arrangements. In conclusion, offering calves with Ca-salt of n-3 FA along with 22% CP content may be related to improved nitrogen efficiency and immune function.

A New Post-Processing Method using Latent Structure Influence Models for Channel Fusion in Automatic Sleep Staging.

Human sleep stage dynamics can be adequately represented using Markov chain models, and the accuracy of sleep stage classification can be improved by considering these dynamics. The present study proposes a new post-processing method based on channel fusion using Latent Structure Influence Models (LSIMs). LSIMs can simultaneously model sequences of different channels to have a nonlinear dynamical fusion based on sleep stage dynamics. The proposed method develops and examines two channel-fusion algorithms: the standard LSIM fusion and the integrated LSIM fusion, in which the latter is more efficient and performs better. The proposed LSIM-based method simultaneously incorporates the nonlinear interactions between channels and the sleep stage dynamics. In the first step, existing sleep staging systems process every data channel independently and produce stage score sequences for each channel. These single-channel scores are then projected into belief space using the marginal one-slice parameter of all channels by LSIM fusion algorithms. The logarithms of marginal one-slice parameters are concatenated to obtain log-scale belief state space (LBSS) features in the standard LSIM fusion. In the integrated LSIM fusion, integrated LBSS (ILBSS) features are formed by combining the LBSS features of several LSIMs. Finally, a KNN classifier maps the LBSS and ILBSS features onto the sleep stages. By utilizing four recently developed sleep staging systems, the proposed method is applied to the publicly available SleepEDF-20 database that contains five AASM sleep stages (N1, N2, N3, REM, and W). Compared to single-channel (Fpz-Cz, Pz-Oz, and EOG) results, integrated LSIM fusion results have a statistically significant improvement of 1.5% in 2-channel fusion (Fpz-Cz and Pz-Oz) and 2.5% in 3-channel fusion (Fpz-Cz, Pz-Oz, and EOG). With an overall accuracy of 87.3% for 3-channel post-processing, the integrated LSIM fusion system offers one of the highest overall accuracy rates among existing studies.

Automatic detection of respiratory events during sleep from Polysomnography data using Layered Hidden Markov Model.

Objective. Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings.Approach. In this study, a hierarchy of HMMs named Layered HMM was presented to detect respiratory events from PSG recordings. The recordings of 210 PSGs from Massachusetts General Hospital's database were used for this study. To develop detection algorithms, extracted feature signals from airflow, movements over the chest and abdomen, and oxygen saturation in blood (SaO2) were chosen as observations. The respiratory disturbance index (RDI) was estimated as the number of apneas, hypopneas, and RERAs per hour of sleep.Main results. The best F1 score of the event by event detection algorithm was between 0.22 ± 0.16 and 0.70 ± 0.08 for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived RDI (R2 = 0.91,p< 0.0001). The best recall of RERA detection was achieved 0.45 ± 0.27.Significance. The results showed that the layered structure can improve the performance of the detection of respiratory events during sleep.

Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification.

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep-wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep-wake classification.

Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model.

Apnea-bradycardia (AB) is a common complication in prematurely born infants, which is associated with reduced survival and neurodevelopmental outcomes. Thus, early detection or predication of AB episodes is critical for initiating preventive interventions. To develop automatic real-time operating systems for early detection of AB, recent advances in signal processing can be employed. Hidden Markov Models (HMM) are probabilistic models with the ability of learning different dynamics of the real time-series such as clinical recordings. In this study, a hierarchy of HMMs named as layered HMM was presented to detect AB episodes from pre-processed single-channel Electrocardiography (ECG). For training the hierarchical structure, RR interval, and width of QRS complex were extracted from ECG as observations. The recordings of 32 premature infants with median 31.2 (29.7, 31.9) weeks of gestation were used for this study. The performance of the proposed layered HMM was evaluated in detecting AB. The best average accuracy of 97.14 ± 0.31% with detection delay of - 5.05 ± 0.41 s was achieved. The results show that layered structure can improve the performance of the detection system in early detecting of AB episodes. Such system can be incorporated for more robust long-term monitoring of preterm infants.

Apnea bradycardia detection based on new coupled hidden semi Markov model.

In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the proposed algorithm was able to detect the desired dynamic with sensitivity of 96.67% and specificity of 98.98%. Furthermore, the method detected the apnea bradycardia episodes with 94.87% sensitivity and 96.52% specificity with mean time delay of 0.73 s. The results show that the algorithm based on CHSMM is a robust tool for monitoring of preterm infants in detecting apnea bradycardia episodes. Graphical Abstract Apnea Bradycardia detection using Coupled hidden semi Markov Model from electrocardiography. In this model, a sequence of hidden states is assigned to each observation based on the effects of previous states of all observations.

The 2017 and 2018 Iranian Brain-Computer Interface Competitions.

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms and discuss the organizational strategies for future campaigns.

Effect of astaxanthin nanoparticles in protecting the post-thawing quality of rooster sperm challenged by cadmium administration.

The protective role of astaxanthin nanoparticles (Ast NPs, 25 mg/kg p.o) against cadmium (Cd, 1 mg/100 g b.w. SC), a known inductor of lipid peroxidation and changes in the antioxidant defense system in the Ross 308 breeder roosters sperm, was examined. Sperm motility (computer-assisted sperm motility analysis), membrane integrity (hypoosmotic swelling test), viability, total abnormality, and enzymatic parameters were assessed after thawing. The testis/body weight (mg/kg) ratio and HE staining results of testis were also performed. The obtained results showed that Cd induced detrimental effects on testis and sperm, while Cd treated by Ast NPs (Cd Ast) diminished this change compared to the Cd group. Cd-treated group resulted in significantly (P < 0.05) lowest total (37.29 ± 2.46) and progressive (5.84 ± 0.47) motility and decreased antioxidant enzyme activity (CAT, TAC, and GPx), as well as producing a significant (P < 0.05) decrease in testis weight (mg) compared to the control group. Treatment with Ast NPs (Ast NPs + Cd) had reversed Cd-induced changes in the antioxidant defense system and significantly prevented Cd-induced testis damage. In conclusion, the results of our work suggest that Ast NPs at 25 mg/kg act as a potent antioxidant in protecting rooster testes against oxidative stress induced by Cd.

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method.

Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others.

Spatial and temporal joint, partially-joint and individual sources in independent component analysis: Application to social brain fMRI dataset.

absectionBackground Three types of sources can be considered in the analysis of multi-subject datasets: (i) joint sources which are common among all subjects, (ii) partially-joint sources which are common only among a subset of subjects, and (iii) individual sources which belong to each subject and represent the specific conditions of that subject. Extracting spatial and temporal joint, partially-joint, and individual sources of multi-subject datasets is of significant importance to analyze common and cross information of multiple subjects. NEW METHOD: We present a new framework to extract these three types of spatial and temporal sources in multi-subject functional magnetic resonance imaging (fMRI) datasets. In this framework, temporal and spatial independent component analysis are utilized, and a weighted sum of higher-order cumulants is maximized. RESULTS: We evaluate the presented algorithm by analyzing simulated data and one real multi-subject fMRI dataset. Our results on the real dataset are consistent with the existing meta-analysis studies. We show that spatial and temporal jointness of extracted joint and partially-joint sources in the theory of mind regions of brain increase with the age of subjects. COMPARISON WITH EXISTING METHOD: In Richardson et al. (2018), predefined regions of interest (ROI) have been used to analyze the real dataset, whereas our unified algorithm simultaneously extracts activated and uncorrelated ROIs, and determines their spatial and temporal jointness without additional computations. CONCLUSIONS: Extracting temporal and spatial joint and partially-joint sources in a unified algorithm improves the accuracy of joint analysis of the multi-subject fMRI dataset.

Joint, Partially-Joint, and Individual Independent Component Analysis in Multi-Subject fMRI Data.

OBJECTIVE: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-joint/individual multiple datasets unidimensional (JpJI-MDU), and accordingly, a source extraction method is developed. METHOD: We present a deflation-based algorithm utilizing higher order cumulants to analyze the JpJI-MDU source model. The algorithm maximizes a cost function which leads to an eigenvalue problem solved with thin-SVD (singular value decomposition) factorization. Furthermore, we introduce the JpJI-feature which indicates the spatial shape of each source and the amount of its jointness with other subjects. We use this feature to determine the type of sources. RESULTS: We evaluate our algorithm by analyzing simulated data and two real functional magnetic resonance imaging (fMRI) datasets. In our simulation study, we will show that the proposed algorithm determines the type of sources with the accuracy of 95% and 100% for 2-class and 3-class clustering scenarios, respectively. Furthermore, our algorithm extracts meaningful joint and partially-joint sources from the two real datasets, which are consistent with the existing neuroscience studies. CONCLUSION: Our results in analyzing the real datasets reveal that both datasets follow the JpJI-MDU source model. This source model improves the accuracy of source extraction methods developed for multi-subject datasets. SIGNIFICANCE: The proposed joint blind source separation algorithm is robust and avoids parameters which are difficult to fine-tune.

Interview based connectivity analysis of EEG in order to detect deception.

Deception is mentioned as an expression or action which hides the truth and deception detection as a concept to uncover the truth. In this research, a connectivity analysis of Electro Encephalography study is presented regarding cognitive processes of an instructed liar/truth-teller about identity during an interview. In this survey, connectivity analysis is applied because it can provide unique information about brain activity patterns of lying and interaction among brain regions. The novelty of this paper lies in applying an open-ended questions interview protocol during EEG recording. We recruited 40 healthy participants to record EEG signal during the interview. For each subject, whole-brain functional and effective connectivity networks such as coherence, generalized partial direct coherence and directed directed transfer function, are constructed for the lie-telling and truth-telling conditions. The classification results demonstrate that lying could be differentiated from truth-telling with an accuracy of 86.25% with the leave-one-person-out method. Results show functional and effective connectivity patterns of lying for the average of all frequency bands are different in regions from that of truth-telling. The current study may shed new light on neural patterns of deception from connectivity analysis view point.

Noninvasive fetal ECG extraction using doubly constrained block-term decomposition.

Fetal electrocardiogram (fECG) monitoring is a beneficial method for assessing fetal health and diagnosing the fetal cardiac condition during pregnancy. In this study, an algorithm is proposed to extract fECG from maternal abdominal signals based on doubly constrained block-term (DoCoBT) tensor decomposition. This tensor decomposition method is constrained by quasiperiodicity constraints of fetal and maternal ECG signals. Tensor decompositions are more powerful tools than matrix decomposition, due to employing more information for source separation. Tensorizing abdominal signals and using periodicity constraints of fetal and maternal ECG, appropriately separates subspaces of the mother, the fetus(es) and noise. The quantitative and qualitative results of the proposed method show improved performance of DoCoBT decomposition versus other tensor and matrix decomposition methods in noisy conditions.

A novel dual and triple shifted RSVP paradigm for P300 speller.

BACKGROUND: A speller system enables disabled people, specifically those with spinal cord injuries, to visually select and spell characters. A problem of primary speller systems is that they are gaze shift dependent. To overcome this problem, a single Rapid Serial Visual Presentation (RSVP) paradigm was initially introduced in which characters are displayed one-by-one at the center of a screen. NEW METHOD: Two new protocols, Dual and Triple shifted RSVP paradigms, are introduced and compared against the single paradigm. In the Dual and Triple paradigms, two and three characters are displayed at the center of the screen simultaneously, holding the advantage of displaying the target character twice and three times respectively, compared to the one-time appearance in the single paradigm. To compare the named paradigms, three subjects participated in experiments using all three paradigms. RESULTS: Offline results demonstrate an average character detection accuracy of 97% for the single and double protocols, and 80% for the Triple paradigm. In addition, average ITR is calculated to be 5.45, 7.62 and 7.90 bit/min for the single, Dual and Triple paradigms respectively. Results identify the Dual RSVP paradigm as the most suitable approach that provides the best balance between ITR and character detection accuracy. COMPARISON WITH EXISTING METHODS: The novel speller system (the Dual paradigm) suggested in this paper demonstrates improved performance compared to existing methods, and overcomes the gaze dependency issue. CONCLUSIONS: Overall, our novel method is a reliable alternative that both removes limitations for users suffering from impaired oculomotor control and improves performance.

Does fennel extract ameliorate oxidative stress frozen-thawed ram sperm?

The aim of this study was to evaluate the quality of ram semen after cryopreservation with different levels of fennel (Foeniculum vulgare) extract (0 (F0), 5 (F5), 10 (F10) and 15 (F15) mg/L) and sperm concentrations (200 (C200) and 400 (C400) × 106 sperm/mL) in a soy lecithin (SL)-based extender. Twenty ejaculates were collected from four ghezel rams and diluted with eight sperm concentrations/fennel combinations: F0C200, F5C200, F10C200, F15C200, F0C400, F5C400, F10C400 and F15C400. Sperm motility, abnormality, plasma membrane, viability, mitochondrial activity, lipid peroxidation (LPO), mitochondrial activity and apoptotic changes were evaluated after freeze-thawing process. It was observed that F10C400 significantly improved total and progressive motility, VSL, membrane integrity of post-thawed ram sperm. MDA level was lower in F5C200 and F10C400 compared to other treatments. The higher percentage of live sperm and the lower percentage of apoptotic sperm were obtained in F10C200 compared to F0C200, F5C200 F15C400, F0C400, F5C400 and F15C400. Extender F10C200 resulted in the highest mitochondria activity compared to the rest of the extenders except F10C400. We conclude that a combination of 10 mg/mL fennel (Foeniculum vulgare) extract and sperm concentration of 200 × 106 sperm/mL can improve the ram semen quality cryopreserved in a soybean lecithin based extender.

Extraction and Automatic Grouping of Joint and Individual Sources in Multisubject fMRI Data Using Higher Order Cumulants.

The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of joint sources by applying a simple and efficient strategy to determine the type of sources (joint or individual). The algorithm also categorizes similar sources automatically across data sets through an optimization process. The proposed algorithm is evaluated by analyzing simulated functional magnetic resonance imaging (fMRI) multisubject data sets, and its performance is compared with existing alternatives. We investigate clean and noisy fMRI signals and consider two source models. Our results reveal that the proposed algorithm outperforms its alternatives in terms of the mean joint signal to interference ratio. We also apply the proposed algorithm on a public-available real fMRI multisubject data set, which was acquired during naturalistic auditory experience. The extracted results are in accordance with the previous studies on naturalistic audio listening and results of a recent study investigated this data set, which demonstrates that the JI-ThICA algorithm can be applied to extract reliable and meaningful information from multisubject fMRI data.

Accessing free quality health care services and premature death.

BACKGROUND: The type of health insurance may affect the likelihood of mortality of insured people. We conducted this study to determine if accessing free quality health care services could decrease the premature mortality of people in a developing country. METHODS: In a multicenter cross sectional study, "years-life-lost" (YLL) due to premature death was evaluated in 202 671 insured people residing in six large regions in Iran. The participants had access to free quality health care services. The number of insured people that died in the six regions during March 20, 2014, to March 20, 2015, as well as their sex, age, and cause of the death, were collected, and the YLL was calculated based on assumptions made in Global Burden of Disease Study 2010 (GBD2010). RESULTS: The crude mortality rate was 2.3 per 1000, significantly lower than the overall rate of 4.6 per 1000 people in general population of Iran. The average YLL was 47 years per 1000 persons, significantly lower than that in general population of Iran and many industrialized countries. The most common causes of death (and YLL) were cardiovascular diseases and malignancies. CONCLUSION: Having access to free quality health care services is associated with a significant decrease in premature death.

Development and Pilot Testing of a Novel Electromechanical Device to Measure Wrist Rigidity in Parkinson's Disease.

Quantitative assessment of the muscle tone is important when studying patients with neurological disorders such as Parkinson's disease (PD). For the assessment of therapeutic progress, quantitative and objective outcome measures are needed. This article presents a novel electromechanical device to monitor the quantitative rigidity of the wrist joint against passive movement. The novel device is equipped with an electrical motor to move the wrist joint in a flexion-extension manner with different velocity profiles. The accuracy of the device was measured in terms of position, velocity and torque accuracy. The feasibility of the measurement procedure was tested in a pilot study with four PD patients and 12 healthy controls (HC), at velocities of 10 °/s,50 °/s, and 100 °/s. {The position and velocity of the developed device were (0.005 ± 0.105)° and (0.734 ±0.276) °/s, unloaded, and (0.003 ± 0.113) ° and (0.013 ± 0.038) °/s, loaded with a relaxed arm, respectively. The torque accuracy was (15.029 ± 2.235) mNm. The comparison of the median rigidity between the PD patients and HC showed significant differences at all tested velocities, during both flexion and extension movements. This device proved to have sufficient accuracy and sensitivity to precisely measure the interaction torque at the wrist joint and to differentiate PD rigidity from normal muscle tone. The device, thus provides a quantitative and objective measure of rigidity in PD.