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Showing papers in "IEEE Transactions on Biomedical Engineering in 2020"


Journal ArticleDOI
TL;DR: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities, which can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain–computer interfaces.
Abstract: Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. Methods: This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. Results: ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. Conclusions: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. Significance: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain–computer interfaces.

230 citations


Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors proposed an approach to align EEG data from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject.
Abstract: Objective : This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain–computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods : We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results : Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion : The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance : Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.

197 citations


Journal ArticleDOI
TL;DR: A novel deep learning approach that is able to successfully address current limitations of standard statistical approaches such as landmarking and joint modeling is developed, and shows that Dynamic-DeepHit provides a drastic improvement in discriminating individual risks of different forms of failures due to cystic fibrosis.
Abstract: Currently available risk prediction methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks This paper develops a novel deep learning approach that is able to successfully address current limitations of standard statistical approaches such as landmarking and joint modeling Our approach, which we call Dynamic-DeepHit, flexibly incorporates the available longitudinal data comprising various repeated measurements (rather than only the last available measurements) in order to issue dynamically updated survival predictions for one or multiple competing risk(s) Dynamic-DeepHit learns the time-to-event distributions without the need to make any assumptions about the underlying stochastic models for the longitudinal and the time-to-event processes Thus, unlike existing works in statistics, our method is able to learn data-driven associations between the longitudinal data and the various associated risks without underlying model specifications We demonstrate the power of our approach by applying it to a real-world longitudinal dataset from the UK Cystic Fibrosis Registry, which includes a heterogeneous cohort of 5883 adult patients with annual follow-ups between 2009 to 2015 The results show that Dynamic-DeepHit provides a drastic improvement in discriminating individual risks of different forms of failures due to cystic fibrosis Furthermore, our analysis utilizes post-processing statistics that provide clinical insight by measuring the influence of each covariate on risk predictions and the temporal importance of longitudinal measurements, thereby enabling us to identify covariates that are influential for different competing risks

120 citations


Journal ArticleDOI
TL;DR: In this article, an electroencephalogram (EEG)-based motor imagery brain-computer interface (MI-BCI) employing visual feedback for upper-limb stroke rehabilitation, and the presence of EEG correlates of mental fatigue during BCI usage was investigated.
Abstract: Objective: This single-arm multisite trial investigates the efficacy of the neurostyle brain exercise therapy towards enhanced recovery (nBETTER) system, an electroencephalogram (EEG)-based motor imagery brain-computer interface (MI-BCI) employing visual feedback for upper-limb stroke rehabilitation, and the presence of EEG correlates of mental fatigue during BCI usage. Methods: A total of 13 recruited stroke patients underwent thrice-weekly nBETTER therapy coupled with standard arm therapy over six weeks. Upper-extremity Fugl–Meyer motor assessment (FMA) scores were measured at baseline (week 0), post-intervention (week 6), and follow-ups (weeks 12 and 24). In total, 11/13 patients (mean age 55.2 years old, mean post-stroke duration 333.7 days, mean baseline FMA 35.5) completed the study. Results: Significant FMA gains relative to baseline were observed at weeks 6 and 24. Retrospectively comparing to the standard arm therapy (SAT) control group and BCI with haptic knob (BCI-HK) intervention group from a previous similar study, the SAT group had no significant gains, whereas the BCI-HK group had significant gains at weeks 6, 12, and 24. EEG analysis revealed significant positive correlations between relative beta power and BCI performance in the frontal and central brain regions, suggesting that mental fatigue may contribute to poorer BCI performance. Conclusion: nBETTER, an EEG-based MI-BCI employing only visual feedback, helps stroke survivors sustain short-term FMA improvement. Analysis of EEG relative beta power indicates that mental fatigue may be present. Significance: This study adds nBETTER to the growing literature of safe and effective stroke rehabilitation MI-BCI, and suggests an additional fatigue-monitoring role in future such BCI.

102 citations


Journal ArticleDOI
TL;DR: Investigation of the effect of incorrect focus cues on the user performance, visual comfort, and workload during the execution of augmented reality (AR)-guided manual task with one of the most advanced OST HMD, the Microsoft HoloLens finds no statistically significant differences in the workload, and in the perceived comfort.
Abstract: Objective: The focal length of available optical see-through (OST) head-mounted displays (HMDs) is at least 2 m; therefore, during manual tasks, the user eye cannot keep in focus both the virtual and real content at the same time. Another perceptual limitation is related to the vergence-accommodation conflict, the latter being present in binocular vision only. This paper investigates the effect of incorrect focus cues on the user performance, visual comfort, and workload during the execution of augmented reality (AR)-guided manual task with one of the most advanced OST HMD, the Microsoft HoloLens. Methods: An experimental study was designed to investigate the performance of 20 subjects in a connect-the-dots task, with and without the use of AR. The following tests were planned: AR-guided monocular and binocular, and naked-eye monocular and binocular. Each trial was analyzed to evaluate the accuracy in connecting dots. NASA Task Load Index and Likert questionnaires were used to assess the workload and the visual comfort. Results: No statistically significant differences were found in the workload, and in the perceived comfort between the AR-guided binocular and monocular test. User performances were significantly better during the naked eye tests. No statistically significant differences in performances were found in the monocular and binocular tests. The maximum error in AR tests was 5.9 mm. Conclusion: Even if there is a growing interest in using commercial OST HMD, for guiding high-precision manual tasks, attention should be paid to the limitations of the available technology not designed for the peripersonal space.

100 citations


Journal ArticleDOI
TL;DR: In this article, a patch-based attention architecture was proposed to provide global context between small, high-resolution patches and a diagnosis-guided loss weighting method that takes the method used for ground-truth annotation into account.
Abstract: Objective: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imbalance encountered in real-world multi-class datasets. Methods: To use high-resolution images, we propose a novel patch-based attention architecture that provides global context between small, high-resolution patches. We modify three pretrained architectures and study the performance of patch-based attention. To counter class imbalance problems, we compare oversampling, balanced batch sampling, and class-specific loss weighting. Additionally, we propose a novel diagnosis-guided loss weighting method that takes the method used for ground-truth annotation into account. Results: Our patch-based attention mechanism outperforms previous methods and improves the mean sensitivity by $\text{7}\%$ . Class balancing significantly improves the mean sensitivity and we show that our diagnosis-guided loss weighting method improves the mean sensitivity by $\text{3}\%$ over normal loss balancing. Conclusion: The novel patch-based attention mechanism can be integrated into pretrained architectures and provides global context between local patches while outperforming other patch-based methods. Hence, pretrained architectures can be readily used with high-resolution images without downsampling. The new diagnosis-guided loss weighting method outperforms other methods and allows for effective training when facing class imbalance. Significance: The proposed methods improve automatic skin lesion classification. They can be extended to other clinical applications where high-resolution image data and class imbalance are relevant.

98 citations


Journal ArticleDOI
TL;DR: A high-speed hybrid BCI system containing as many as 108 instructions, which were encoded by concurrent P300 and steady-state visual evoked potential (SSVEP) features and decoded by an ensemble task-related component analysis method significantly improves the degree of freedom of BCIs.
Abstract: Objective: Recently, electroencephalography (EEG)- based brain-computer interfaces (BCIs) have made tremendous progress in increasing communication speed. However, current BCI systems could only implement a small number of command codes, which hampers their applicability. Methods: This study developed a high-speed hybrid BCI system containing as many as 108 instructions, which were encoded by concurrent P300 and steady-state visual evoked potential (SSVEP) features and decoded by an ensemble task-related component analysis method. Notably, besides the frequency-phase-modulated SSVEP and time-modulated P300 features as contained in the traditional hybrid P300 and SSVEP features, this study found two new distinct EEG features for the concurrent P300 and SSVEP features, i.e., time-modulated SSVEP and frequency-phase- modulated P300. Ten subjects spelled in both offline and online cued-guided spelling experiments. Other ten subjects took part in online copy-spelling experiments. Results: Offline analyses demonstrate that the concurrent P300 and SSVEP features can provide adequate classification information to correctly select the target from 108 characters in 1.7 seconds. Online cued-guided spelling and copy-spelling tests further show that the proposed BCI system can reach an average information transfer rate (ITR) of 172.46 ± 32.91 bits/min and 164.69 ± 33.32 bits/min respectively, with a peak value of 238.41 bits/min (The demo video of online copy-spelling can be found at https://www.youtube.com/watch?v=EW2Q08oHSBo ). Conclusion: We expand a BCI instruction set to over 100 command codes with high-speed in an efficient manner, which significantly improves the degree of freedom of BCIs. Significance: This study hold promise for broadening the applications of BCI systems.

96 citations


Journal ArticleDOI
TL;DR: The proposed JointRCNN model outperforms state-of-the-art methods for optic disc and cup segmentation task and glaucoma detection task and is promising to be used for glAUcoma screening.
Abstract: Objective: The purpose of this paper is to propose a novel algorithm for joint optic disc and cup segmentation, which aids the glaucoma detection. Methods: By assuming the shapes of cup and disc regions to be elliptical, we proposed an end-to-end region-based convolutional neural network for joint optic disc and cup segmentation (referred to as JointRCNN). Atrous convolution is introduced to boost the performance of feature extraction module. In JointRCNN, disc proposal network (DPN) and cup proposal network (CPN) are proposed to generate bounding box proposals for the optic disc and cup, respectively. Given the prior knowledge that the optic cup is located in the optic disc, disc attention module is proposed to connect DPN and CPN, where a suitable bounding box of the optic disc is first selected and then continued to be propagated forward as the basis for optic cup detection in our proposed network. After obtaining the disc and cup regions, which are the inscribed ellipses of the corresponding detected bounding boxes, the vertical cup-to-disc ratio is computed and used as an indicator for glaucoma detection. Results: Comprehensive experiments clearly show that our JointRCNN model outperforms state-of-the-art methods for optic disc and cup segmentation task and glaucoma detection task. Conclusion: Joint optic disc and cup segmentation, which utilizes the connection between optic disc and cup, could improve the performance of optic disc and cup segmentation. Significance: The proposed method improves the accuracy of glaucoma detection. It is promising to be used for glaucoma screening.

85 citations


Journal ArticleDOI
TL;DR: The in-ear sensor is feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG, and represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring.
Abstract: Objective: Advances in sensor miniaturization and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: A total of 22 healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography recordings. The ear-EEG data were analyzed in the both structural complexity and spectral domains. The extracted features were used for automatic sleep stage prediction through supervized machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification. This is supported by a substantial agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG. It, therefore, represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The “standardized” one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep—this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

83 citations


Journal ArticleDOI
TL;DR: A novel tactile P300 BCI paradigm is proposed for further expanding the tactile stimulation methods and might lead to many promising applications of such BCIs.
Abstract: Objective: Tactile brain-computer interface (BCI) systems can provide new communication and control options for patients with impairments of eye movements or vision. One of the most common modalities used in these BCIs is the P300 potential. Until now, tactile P300 BCIs have been successfully constructed by situating tactile stimuli at various parts of the human body. This article proposed a novel tactile P300 BCI paradigm for further expanding the tactile stimulation methods. Methods: In our proposed paradigm, the spatial target vibrotactile stimuli were delivered to subject's left and right cheeks. To validate the feasibility of our proposed paradigm, a traditional tactile P300 BCI paradigm employing spatial target vibrotactile stimuli to subject's left and right wrists was used for comparison. Results: The experimental results of nine healthy subjects demonstrated that the proposed paradigm could obtain significantly higher classification accuracy and information transfer rate than the traditional paradigm (both for p Conclusion: The proposed tactile P300 BCI paradigm is feasible, and can bring about superior performance and use-evaluation. Significance: The new paradigm might lead to many promising applications of such BCIs.

75 citations


Journal ArticleDOI
TL;DR: In this paper, a single-sided sound speed inversion solution using a fully convolutional deep neural network was proposed to detect tissue elasticity in soft tissue at high frame rates.
Abstract: Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for things such as, cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state-of-the-art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound hardware due to high-power requirements, and are extremely sensitive to patient and sonographer motion, and generally suffer from low frame rates. Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single-sided pressure-wave sound speed measurements from channel data. Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data. Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data. Conclusion: Sound speed inversion on channel data has made significant potential possible in real time with deep learning technologies. Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. Longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are also possible.

Journal ArticleDOI
TL;DR: A unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series is proposed, which incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model.
Abstract: Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate the proposed method on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with results suggesting the effectiveness of our method in both tasks of disease progression prediction and AD-related hub detection.

Journal ArticleDOI
TL;DR: BCI-SRG suggested probable trends of sustained functional improvements with peculiar kinesthetic experience outlasting active intervention in chronic stroke despite the dire need for large-scale investigations to verify statistical significance.
Abstract: Objective: This randomized controlled feasibility study investigates the ability for clinical application of the Brain-Computer Interface-based Soft Robotic Glove (BCI-SRG) incorporating activities of daily living (ADL)-oriented tasks for stroke rehabilitation. Methods: Eleven recruited chronic stroke patients were randomized into BCI-SRG or Soft Robotic Glove (SRG) group. Each group underwent 120-minute intervention per session comprising 30-minute standard arm therapy and 90-minute experimental therapy (BCI-SRG or SRG). To perform ADL tasks, BCI-SRG group used motor imagery-BCI and SRG, while SRG group used SRG without motor imagery-BCI. Both groups received 18 sessions of intervention over 6 weeks. Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores were measured at baseline (week 0), post- intervention (week 6), and follow-ups (week 12 and 24). In total, 10/11 patients completed the study with 5 in each group and 1 dropped out. Results: Though there were no significant intergroup differences for FMA and ARAT during 6-week intervention, the improvement of FMA and ARAT seemed to sustain beyond 6-week intervention for BCI-SRG group, as compared with SRG control. Incidentally, all BCI-SRG subjects reported a sense of vivid movement of the stroke-impaired upper limb and 3/5 had this phenomenon persisting beyond intervention while none of SRG did. Conclusion : BCI-SRG suggested probable trends of sustained functional improvements with peculiar kinesthetic experience outlasting active intervention in chronic stroke despite the dire need for large-scale investigations to verify statistical significance. Significance: Addition of BCI to soft robotic training for ADL-oriented stroke rehabilitation holds promise for sustained improvements as well as elicited perception of motor movements.

Journal ArticleDOI
TL;DR: This study compared a recently developed algorithm, i.e., discriminative canonical pattern matching (DCPM), with seven ERP-BCI classification methods for the single-trial classification of two private EEG datasets and three public EEG datasets with small training sets and showed that the DCPM outperformed other classifiers for all of the tested datasets, suggesting theDCPM is a robust classification algorithm for assessing a wide range of ERP components.
Abstract: Event-related potentials (ERPs) are one of the most popular control signals for brain–computer interfaces (BCIs). However, they are very weak and sensitive to the experimental settings including paradigms, stimulation parameters and even surrounding environments, resulting in a diversity of ERP patterns across different BCI experiments. It's still a challenge to develop a general decoding algorithm that can adapt to the ERP diversities of different BCI datasets with small training sets. This study compared a recently developed algorithm, i.e., discriminative canonical pattern matching (DCPM), with seven ERP-BCI classification methods, i.e., linear discriminant analysis (LDA), stepwise LDA, bayesian LDA, shrinkage LDA, spatial-temporal discriminant analysis (STDA), xDAWN and EEGNet for the single-trial classification of two private EEG datasets and three public EEG datasets with small training sets. The feature ERPs of the five datasets included P300, motion visual evoked potential (mVEP), and miniature asymmetric visual evoked potential (aVEP). Study results showed that the DCPM outperformed other classifiers for all of the tested datasets, suggesting the DCPM is a robust classification algorithm for assessing a wide range of ERP components.

Journal ArticleDOI
TL;DR: There was no significant advantage observed from the ML models compared to the classic ARX model in predicting BG levels for T1D, except that TCN's performance was more robust with respect to BG trajectories with spurious oscillations.
Abstract: Objective: This paper aims to compare the performance of several commonly known machine-learning (ML) models versus a classic Autoregression with Exogenous inputs (ARX) model in the prediction of blood glucose (BG) levels using time-series data of patients with Type 1 diabetes (T1D). Methods: The ML algorithms include ML-based regression models and deep learning models such as a vanilla Long-Short-Term-Memory (LSTM) Network and a Temporal Convolution Network (TCN). Evaluations have been conducted with respect to different input features, regression model orders, as well as using the recursive method or direct method for multi-step prediction of BG levels. Prediction performance metrics include the average Root Mean Square Error (RMSE), temporal gain (TG) for early prediction, and the normalized energy of the second-order differences (ESOD) of the predicted time series to reflect risk of false alerts on hypo/hyper glycemia events. Results: The ARX model achieved the lowest average RMSE for both recursive and direct methods, the second highest average TG under the direct method, but with a higher average normalized ESOD than some other models. Conclusion: There was no significant advantage observed from the ML models compared to the classic ARX model in predicting BG levels for T1D, except that TCN's performance was more robust with respect to BG trajectories with spurious oscillations, for which ARX tended to over-predict peak BG values and under-predict valley BG values. Significance: Insight learned from this study could help researchers and clinical practitioners to select appropriate models for BG prediction.

Journal ArticleDOI
TL;DR: The colloidal microswarm is real-time localized and navigated using ultrasound feedback, which shows great potential for biomedical applications that require real- time noninvasive tracking.
Abstract: Objective: Untethered microrobots hold great promise for applications in biomedical field including targeted delivery, biosensing, and microsurgery. A major challenge of using microrobots to perform in vivo tasks is the real-time localization and motion control using medical imaging technologies. Here we report real-time magnetic navigation of a paramagnetic nanoparticle-based microswarm under ultrasound guidance. Methods: A three-axis Helmholtz electromagnetic coil system integrated with an ultrasound imaging system is developed for generation, actuation, and closed-loop control of the microswarm. The magnetite nanoparticle-based microswarm is generated and navigated using rotating magnetic fields. In order to localize the microswarm in real time, the dynamic imaging contrast has been analyzed and exploited in image process to increase the signal-to-noise ratio. Moreover, imaging of the microswarm at different depths are experimentally studied and analyzed, and the minimal dose of nanoparticles for localizing a microswarm at different depths is ex vivo investigated. For real-time navigating the microswarm in a confined environment, a PI control scheme is designed. Results: Image differencing-based processing increases the signal-to-noise ratio, and the microswarm can be ex vivo localized at depth of 2.2–7.8 cm. Experimental results show that the microswarm is able to be real-time navigated along a planned path in a channel, and the average steady-state error is 0.27 mm ( $\sim$ 33.7% of the body length). Significance: The colloidal microswarm is real-time localized and navigated using ultrasound feedback, which shows great potential for biomedical applications that require real-time noninvasive tracking.

Journal ArticleDOI
TL;DR: This study suggests that widely pursued PPG-based BP measurement devices including those that detect PTT should maintain the CP or include a CP measurement in the calibration equation for deriving BP.
Abstract: Objective: Photo-plethysmography (PPG) sensors are often used to detect pulse transit time (PTT) for potential cuff-less blood pressure (BP) measurement. It is known that the contact pressure (CP) of the PPG sensor markedly alters the PPG waveform amplitude. The objective was to test the hypothesis that PTT detected via PPG sensors is likewise impacted by CP. Methods: A device was built to measure the time delay between ECG and finger PPG waveforms (i.e., pulse arrival time (PAT) – a popular surrogate of PTT) and the PPG sensor CP at different CP levels. These measurements and finger cuff BP were recorded while the CP was slowly varied in 17 healthy subjects. Results: Over a physiologic range of CP, the maximum deviations of PAT detected at the PPG foot and peak were 22±2 and 40±7 ms (p Conclusion: Since the regulatory bias error for BP measurement is limited to 5 mmHg, PPG sensor CP should be taken into account for cuff-less BP measurement via PTT. Significance: This study suggests that widely pursued PPG-based BP measurement devices including those that detect PTT should maintain the CP or include a CP measurement in the calibration equation for deriving BP.

Journal ArticleDOI
TL;DR: A unified framework under which the spatial filtering algorithms can be formulated as generalized eigenvalue problems (GEPs) with four different elements: data, temporal filter, orthogonal projection and spatial filter is proposed and designed for improvements through the choice of different elements.
Abstract: Objective: In the steady-state visual evoked potential (SSVEP)-based brain computer interfaces (BCIs), spatial filtering, which combines the multi-channel electroencephalography (EEG) signals in order to reduce the non-SSVEP-related component and thus enhance the signal-to-noise ratio (SNR), plays an important role in target recognition. Recently, various spatial filtering algorithms have been developed employing different prior knowledge and characteristics of SSVEPs, however how these algorithms interconnect and differ is not yet fully explored, leading to difficulties in further understanding, utilizing and improving them. Methods: We propose a unified framework under which the spatial filtering algorithms can be formulated as generalized eigenvalue problems (GEPs) with four different elements: data, temporal filter, orthogonal projection and spatial filter. Based on the framework, we design new spatial filtering algorithms for improvements through the choice of different elements. Results: The similarities, differences and relationships among nineteen mainstream spatial filtering algorithms are revealed under the proposed framework. Particularly, it is found that they originate from the canonical correlation analysis (CCA), principal component analysis (PCA), and multi-set CCA, respectively. Furthermore, three new spatial filtering algorithms are developed with enhanced performance validated on two public SSVEP datasets with 45 subjects. Conclusion: The proposed framework provides insights into the underlying relationships among different spatial filtering algorithms and helps the design of new spatial filtering algorithms. Significance: This is a systematic study to explore, compare and improve the existing spatial filtering algorithms, which would be significant for further understanding and future development of high performance SSVEP-based BCIs.

Journal ArticleDOI
TL;DR: The proposed method is capable of simultaneously and continuously estimating multi-joint movements of the human arm in real-time by characterizing the nonlinear mappings between muscle activity and kinematic signals based on linear regression.
Abstract: Objective: This study aimed to decode shoulder, elbow and wrist dynamic movements continuously and simultaneously based on multi-channel surface electromyography signals, useful for electromyography controlled exoskeleton robots for upper-limb rehabilitation. Methods: Ten able-bodied subjects and ten stroke subjects were instructed to voluntarily move the shoulder, elbow and wrist joints back and forth in a horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface electromyography signals from six muscles crossing the joints were recorded. A set of three parallel linear-nonlinear cascade decoders was developed to continuously estimate the selected shoulder, elbow and wrist movements based on a generalized linear model using the anterior deltoid, posterior deltoid, biceps brachii, long head triceps brachii, flexor carpi radialis, and extensor carpi radialis muscle electromyography signals as the model inputs. Results: The decoder performed well for both healthy and stroke populations. As movement smoothness decreased, decoding performance decreased for the stroke population. Conclusion: The proposed method is capable of simultaneously and continuously estimating multi-joint movements of the human arm in real-time by characterizing the nonlinear mappings between muscle activity and kinematic signals based on linear regression. Significance: This may prove useful in developing myoelectric controlled exoskeletons for motor rehabilitation of neurological disorders.

Journal ArticleDOI
TL;DR: A patient dependent rotated linear-kernel support vector machine classifier that combines the global and local classifiers, with three types of feature vectors extracted directly from the Delta modulated bit-streams is proposed.
Abstract: Real-time wearable electrocardiogram monitoring sensor is one of the best candidates in assisting cardiovascular disease diagnosis. In this paper, we present a novel real-time machine learning system for Arrhythmia classification. The system is based on the parallel Delta modulation and QRS/PT wave detection algorithms. We propose a patient dependent rotated linear-kernel support vector machine classifier that combines the global and local classifiers, with three types of feature vectors extracted directly from the Delta modulated bit-streams. The performance of the proposed system is evaluated using the MIT-BIH Arrhythmia database. According to the AAMI standard, two binary classifications are performed and evaluated, which are supraventricular ectopic beat (SVEB) versus the rest four classes, and ventricular ectopic beat (VEB) versus the rest. For SVEB classification, the preferred SkP-32 method's F1 score, sensitivity, specificity, and positive predictivity value are 0.83, 79.3%, 99.6%, and 88.2%, respectively, and for VEB classification, the numbers are 0.92%, 92.8%, 99.4%, and 91.6%, respectively. The results show that the performance of our proposed approach is comparable to that of published research. The proposed low-complexity algorithm has the potential to be implemented as an on-sensor machine learning solution.

Journal ArticleDOI
TL;DR: The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPks compared to passive MPks or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics.
Abstract: Goal: To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs). Methods: Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well. Results: Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope. Conclusion: The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori. Significance: This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market.

Journal ArticleDOI
TL;DR: A fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG).
Abstract: Objective: We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Methods: Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. Results: We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36–100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using $k$ -fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9 s versus 14.7 s). Moreover, the algorithm can reliably identify (with a $p$ -value $ ) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Conclusion and significance: Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch .

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TL;DR: Dual-layer EEG enabled us to isolate changes in human sensorimotor electrocortical dynamics across walking speeds, and allowed us to document and rule out residual artifacts, which exposed sensorsimotor spectral power changes across gait speeds.
Abstract: Objective: Our aim was to determine if walking speed affected human sensorimotor electrocortical dynamics using mobile high-density electroencephalography (EEG). Methods: To overcome limitations associated with motion and muscle artifact contamination in EEG recordings, we compared solutions for artifact removal using novel dual-layer EEG electrodes and alternative signal processing methods. Dual-layer EEG simultaneously recorded human electrocortical signals and isolated motion artifacts using pairs of mechanically coupled and electrically independent electrodes. For electrical muscle activity removal, we incorporated electromyographic (EMG) recordings from the neck into our mobile EEG data processing pipeline. We compared artifact removal methods during treadmill walking at four speeds (0.5, 1.0, 1.5, and 2.0 m/s). Results: Left and right sensorimotor alpha and beta spectral power increased in contralateral limb single support and push off, and decreased during contralateral limb swing at each speed. At faster walking speeds, sensorimotor spectral power fluctuations were less pronounced across the gait cycle with reduced alpha and beta power ( p Conclusion and significance: Dual-layer EEG enabled us to isolate changes in human sensorimotor electrocortical dynamics across walking speeds. A comparison of signal processing approaches revealed similar intrastride cortical fluctuations when applying common (e.g., artifact subspace reconstruction) and novel artifact rejection methods. Dual-layer EEG, however, allowed us to document and rule out residual artifacts, which exposed sensorimotor spectral power changes across gait speeds.

Journal ArticleDOI
TL;DR: The applicability of the proposed sensor has been validated and have the potential to use for routine diagnosis, and the parameters responsible for the performance of OFS, such as sensitivity, detection limit, and selectivity are greatly improved.
Abstract: This article presents a localized surface plasmon resonance (LSPR) phenomenon based optical fiber sensor (OFS) for the detection of dopamine (DA). DA functions as a hormone and a neurotransmitter in the human body and plays a crucial role in the peripheral system. To develop the OFS for DA detection, taper fiber probe was fabricated and immobilized with silver nanoparticles (AgNPs) and functionalized with Polyethylene glycol (PEG). The developed sensor shows the great selectivity in the presence of ascorbic acid (AA) oxidation due to PEG coating. The morphology of the AgNPs and uniformity of coating over the surface of sensing probe were confirmed with UV-visible spectrophotometer, transmission electron microscope (TEM), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscope (SEM). The calibration curve is found to be linear over the range of 10 nM−1 μM with the lowest detection limit of 0.058 μM. Also provides a wide dynamic range of detection (10 nm–100 μM). The parameters responsible for the performance of OFS, such as sensitivity, detection limit, and selectivity are greatly improved in the proposed sensor. The applicability of the proposed sensor has been validated and have the potential to use for routine diagnosis.

Journal ArticleDOI
TL;DR: A new PPG-extraction method, DIScriminative signature based extraction (DIS), to significantly improve the pulse-rate measurement in NIR by using one-step least-squares regression to retrieve the pulsatile component from color signals and suppress disturbance signals simultaneously.
Abstract: Near-infrared (NIR) remote photoplethysmography (PPG) promises attractive applications in darkness, as it involves unobtrusive, invisible light. However, since the PPG strength (AC/DC) is much lower in the NIR spectrum than in the RGB spectrum, robust vital signs monitoring is more challenging. In this paper, we propose a new PPG-extraction method, DIScriminative signature based extraction (DIS), to significantly improve the pulse-rate measurement in NIR. Our core idea is to use both the color signals containing blood absorption variations and additional disturbance signals as input for PPG extraction. By defining a discriminative signature, we use one-step least-squares regression (joint optimization) to retrieve the pulsatile component from color signals and suppress disturbance signals simultaneously. A large-scale lab experiment, recorded in NIR with heavy body motions, shows the significant improvement of DIS over the state-of-the-art method, whereas its principle is simple and generally applicable.

Journal ArticleDOI
TL;DR: A novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials in the EEG signal and blink-related electrooculography (EOG) signals that generates multiple commands with a high ITR and low FPR has great potential for practical applications in communication and control.
Abstract: Objective: A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals. Methods: Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results. Results: Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34 s and an average false-positive rate (FPR) of 0.8%. Conclusion: The proposed BCI generates multiple commands with a high ITR and low FPR. Significance: The hybrid asynchronous BCI has great potential for practical applications in communication and control.

Journal ArticleDOI
TL;DR: WESN-like platforms allow us to achieve similar AAD performance as with long-distance EEG recordings while adhering to the stringent miniaturization constraints for ambulatory EEG.
Abstract: Objective: Concealable, miniaturized electroencephalography (mini-EEG) recording devices are crucial enablers toward long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. We investigate whether optimizing the WESN topology can compensate for miniaturization effects in an auditory attention detection (AAD) paradigm. Methods: Starting from standard full-cap high-density EEG data, we emulate several candidate mini-EEG sensor nodes that locally collect EEG data with embedded electrodes separated by short distances. We propose a greedy group-utility based channel selection strategy to select a subset of these candidate nodes to form a WESN. We compare the AAD performance of this WESN with the performance obtained using long-distance EEG recordings. Results: The AAD performance using short-distance EEG measurements is comparable to using an equal number of long-distance EEG measurements if, in both cases, the optimal electrode positions are selected. A significant increase in performance was found when using nodes with three electrodes over nodes with two electrodes. Conclusion: When the nodes are optimally placed, WESNs do not significantly suffer from EEG miniaturization effects in the case of AAD. Significance: WESN-like platforms allow us to achieve similar AAD performance as with long-distance EEG recordings while adhering to the stringent miniaturization constraints for ambulatory EEG. Their applicability in an AAD task is important for the design of neuro-steered auditory prostheses.

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TL;DR: Computer simulations suggest that the proposed STIMULUS strategy has potential to achieve noninvasive electrical deep brain stimulation with high spatial precision and has the flexibility of generating rich stimulation patterns.
Abstract: Objective: This paper obtains strategies that can achieve spatially precise noninvasive deep brain stimulation using electrical currents. Methods: We provide the Spatio-Temporal Interference-based stiMULation focUsing Strategy (STIMULUS) that generates rich patterns of spatiotemporally interfering currents to stimulate precisely and deep inside the brain. To calibrate and compare the accuracy of stimulation using different techniques, we utilize computational Hodgkin–Huxley-type models for neurons and a model of current dispersion in the head. Results: In this computational model, STIMULUS dramatically outperforms the recently proposed Temporal Interference (TI) stimulation strategy in spatial precision. Our results also suggest that STIMULUS can attain steerable and multisite stimulation, which can be important in giving feedback in brain–machine interfaces. Finally, by examining more mammalian neuron types, we also observe that not every neuron exhibits temporal-interference stimulation. Conclusions: Computer simulations suggest that the proposed STIMULUS strategy has potential to achieve noninvasive electrical deep brain stimulation with high spatial precision and, further, has the flexibility of generating rich stimulation patterns. The fact that some neuron types do not exhibit TI stimulation suggests that caution is needed in evaluating conclusions of application of TI stimulation on large mammalian brains. Significance: A technique to reliably, noninvasively, and precisely stimulate deep inside the human brain could revolutionize human neuroscience and clinical treatments. We obtain the first computational demonstration of the recently proposed TI stimulation. Advancing on that, we propose a novel strategy that can perform stimulation with high precision and flexibility.

Journal ArticleDOI
TL;DR: A new photoacoustic tomography system that provides visualization of angiographic features in a human breast with mammogram-like images that possesses the benefits of portability, speedy scanning, and patient comfort is presented.
Abstract: Objective: We present a new photoacoustic tomography system that provides visualization of angiographic features in a human breast with mammogram-like images. Methods: The system images a mildly compressed breast, from both top and bottom, using two 128-element, 2.25 MHz linear transducer arrays and line optical fiber bundles. The mild compression is achieved using plastic films, which is a more comfortable experience for the patient compared to rigid metal plates used in a traditional mammogram. Results: We could image a D cup-sized breast of 7 cm thickness within 1 minute and achieve a spatial resolution of around 1 mm in all directions. Conclusion: Our system possesses the benefits of portability, speedy scanning, and patient comfort. The craniocaudal-view images can be easily correlated with existing imaging modalities for data interpretation. Significance: Early cancer detection plays a critical role in overall cancer survival rate. Our system may address the limitations of mammogram and offer a radiation-free screening technique for patients with dense breasts.

Journal ArticleDOI
TL;DR: Results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies.
Abstract: A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies.