One out of ten independent components shows flipped polarity with poorer data quality: EEG database study.

Independent component analysis (ICA) is widely used today for scalp-recorded EEG analysis. One of the limitations of ICA-based analysis is polarity indeterminacy. It is not easy to find detailed documentations that explains engineering solutions of how the polarity indeterminacy is addressed in a given implementation. We investigated how it is implemented in the case of EEGLAB and also the relation between the outcome of the polarity determination and classification of independent components (ICs) in terms of the estimated nature of the sources (brain, muscle, eye, etc.) using an open database of n = 212 EEG dataset of resting state recordings. We found that (1) about 91% of ICs showed positive-dominant IC scalp topographies; (2) positive-dominant ICs were more associated with brain-originated signals; (3) positive-dominant ICs showed more radial (peaked at 10-30 degrees deviations from the radial axis) dipolar projection pattern with less residual variance from fitting the equivalent current dipole. In conclusion, using the EEGLAB's default ICA algorithm, one out of 10 ICs results in flipping its polarity to negative, which is associated with non-radial dipole orientation with higher residual variance. Thus, we determined EEGLAB biases toward positive polarity in decomposing high-quality brain ICs.

Stimulus Design for Visual Evoked Potential Based Brain-Computer Interfaces.

Visual stimuli design plays an important role in brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs). Variations in stimulus parameters have been shown to affect both decoding accuracy and subjective perception experience, implying the need for a trade-off in design. In this study, we comprehensively and systematically compared various combinations of amplitude contrast and spectral content parameters in the stimulus design to quantify their impact on decoding performance and subject comfort. Specifically, three parameters were investigated: 1) contrast level, 2) temporal pattern (periodic steady-state or pseudo-random code-modulated), and 3) frequency range. We collected electroencephalogram (EEG) data and subjective perception ratings from ten subjects and evaluated the decoding accuracy and subject comfort rating for different combinations of the stimulus parameters. Our results indicate that while high-frequency steady-state VEP (SSVEP) stimuli were rated the most comfortable, they also had the lowest decoding accuracy. Conversely, low-frequency SSVEP stimuli were rated the least comfortable but had the highest decoding accuracy. Standard and high-frequency M-sequence code-modulated VEPs (c-VEPs) produced intermediates between the two. We observed a consistent trade-off relationship between decoding accuracy and subjective comfort level across all parameters. Based on our findings, we offer c-VEP as a preferable stimulus for achieving reliable decoding accuracy while maintaining a reasonable level of comfortability.

Novel Moving Steady-State Visual Evoked Potential Stimulus to Assess Afferent and Efferent Dysfunction in Multiple Sclerosis.

Afferent and efferent visual dysfunction are prominent features of multiple sclerosis (MS). Visual outcomes have been shown to be robust biomarkers of the overall disease state. Unfortunately, precise measurement of afferent and efferent function is typically limited to tertiary care facilities, which have the equipment and analytical capacity to make these measurements, and even then, only a few centers can accurately quantify both afferent and efferent dysfunction. These measurements are currently unavailable in acute care facilities (ER, hospital floors). We aimed to develop a moving multifocal steady-state visual evoked potential (mfSSVEP) stimulus to simultaneously assess afferent and efferent dysfunction in MS for application on a mobile platform. The brain-computer interface (BCI) platform consists of a head-mounted virtual-reality headset with electroencephalogram (EEG) and electrooculogram (EOG) sensors. To evaluate the platform, we recruited consecutive patients who met the 2017 MS McDonald diagnostic criteria and healthy controls for a pilot cross-sectional study. Nine MS patients (mean age 32.7 years, SD 4.33) and ten healthy controls (24.9 years, SD 7.2) completed the research protocol. The afferent measures based on mfSSVEPs showed a significant difference between the groups (signal-to-noise ratio of mfSSVEPs for controls: 2.50 ± 0.72 vs. MS: 2.04 ± 0.47) after controlling for age (p = 0.049). In addition, the moving stimulus successfully induced smooth pursuit movement that can be measured by the EOG signals. There was a trend for worse smooth pursuit tracking in cases vs. controls, but this did not reach nominal statistical significance in this small pilot sample. This study introduces a novel moving mfSSVEP stimulus for a BCI platform to evaluate neurologic visual function. The moving stimulus showed a reliable capability to assess both afferent and efferent visual functions simultaneously.

Revisiting Polarity Indeterminacy of ICA-Decomposed ERPs and Scalp Topographies.

We propose an alternative method to align the polarities of independent components (ICs) for group-level IC cluster analysis. Current methods are presently limited in how indeterminacy of IC polarities is handled, as when multiplying a weight matrix to a time-series IC activation, the result from 1 × 1 and - 1 × - 1 are indistinguishable. We first clarify the EEGLAB's default solution and define it as the iterative correlation maximization as it maximizes the within-cluster correlations of the IC scalp topographies to the cluster mean. We then propose the covariance maximization method, which determines the polarity of ICs based on the sign of the largest eigenvalue of covariance matrix. We compared the two methods on datasets from a published visual event-related potential (ERP) study. The results were similar when both methods were applied to the IC scalp topographies. However, when the proposed method was applied to IC ERPs, the number of clusters that showed significant ERP amplitudes increased from 5 to 9 out of 9 due to minimization of within-cluster ERP amplitude cancellation. Our study confirm covariance maximization provides an alternative solution to post-ICA group-level analysis that can maximize sensitivity of IC ERPs.

Clinical characteristics and treatment outcomes of carbapenem-resistant Enterobacterales infections in Japan.

OBJECTIVES: The dissemination of difficult-to-treat carbapenem-resistant Enterobacterales (CRE) is of great concern. We clarified the risk factors underlying CRE infection mortality in Japan. METHODS: We conducted a retrospective, multicentre, observational cohort study of patients with CRE infections at 28 university hospitals from September 2014 to December 2016, using the Japanese National Surveillance criteria. Clinical information, including patient background, type of infection, antibiotic treatment, and treatment outcome, was collected. The carbapenemase genotype was determined using PCR sequencing. Multivariate analysis was performed to identify the risk factors for 28-day mortality. RESULTS: Among the 179 patients enrolled, 65 patients (36.3%) had bloodstream infections, with 37 (20.7%) infections occurring due to carbapenemase-producing Enterobacterales (CPE); all carbapenemases were of IMP-type (IMP-1: 32, IMP-6: 5). Two-thirds of CPE were identified as Enterobacter cloacae complex. Combination therapy was administered only in 46 patients (25.7%), and the 28-day mortality rate was 14.3%. Univariate analysis showed that solid metastatic cancer, Charlson Comorbidity Index ≥3, bloodstream infection, pneumonia, or empyema, central venous catheters, mechanical ventilation, and prior use of quinolones were significant risk factors for mortality. Multivariate analysis revealed that mechanical ventilation (OR: 6.71 [1.42-31.6], P = 0.016), solid metastatic cancers (OR: 5.63 [1.38-23.0], P = 0.016), and bloodstream infections (OR: 3.49 [1.02-12.0], P = 0.046) were independent risk factors for 28-day mortality. CONCLUSION: The significant risk factors for 28-day mortality in patients with CRE infections in Japan are mechanical ventilation, solid metastatic cancers, and bloodstream infections.

Differences in the sequence of PlcR transcriptional regulator-binding site affect sphingomyelinase production in Bacillus cereus.

Bacillus cereus is an opportunistic pathogen that often causes severe infections such as bacteremia, with sphingomyelinase (SMase) being a crucial virulence factor. Although many strains of B. cereus carry the SMase gene, they are classified as SMase-producing and nonproducing strains. The reason for different SMase production among B. cereus strains remains unknown. In this study, we investigated the relationship between SMase and the PlcR transcriptional regulation system to clarify the mechanism leading to varied SMase production among B. cereus strains. We analyzed the sequence of the PlcR box, which is a transcriptional regulator-binding site, located at the promoter region of SMase and phosphatidylcholine-specific phospholipase C. Based on differences in the PlcR box sequences, we classified the B. cereus strains into three groups (I, II, and III). SMase expression and activity were hardly detected in Group III strains. In Group I strains, SMase activity and its expression were maximal at the onset of the stationary phase and decreased during the stationary phase, whereas those were maintained during the stationary phase in Group II stains. On injection of B. cereus strains into mice or incubation with macrophages for phagocytosis assay, the SMase-producing Group I and II strains showed higher pathogenicity than Group III strains. These findings suggest that PlcR box sequence in B. cereus affects the production of SMase, which may provide important clinical information for the detection of highly pathogenic B. cereus strains.

Online Adaptation Boosts SSVEP-Based BCI Performance.

OBJECTIVE: A user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) prefers no calibration for its target recognition algorithm, however, the existing calibration-free schemes perform still far behind their calibration-based counterparts. To tackle this issue, learning online from the subject's unlabeled data is investigated as a potential approach to boost the performance of the calibration-free SSVEP-based BCIs. METHODS: An online adaptation scheme is developed to tune the spatial filters using the online unlabeled data from previous trials, and then developing the online adaptive canonical correlation analysis (OACCA) method. RESULTS: A simulation study on two public SSVEP datasets (Dataset I and II) with a total of 105 subjects demonstrated that the proposed online adaptation scheme can boost the CCA's averaged information transfer rate (ITR) from 94.60 to 158.87 bits/min in Dataset I and from 85.80 to 123.91 bits/min in Dataset II. Furthermore, in our online experiment it boosted the CCA's ITR from 55.81 bits/min to 95.73 bits/min. More importantly, this online adaptation scheme can be easily combined with any spatial filtering-based algorithms to achieve online learning. CONCLUSION: By online adaptation, the proposed OACCA performed much better than the calibration-free CCA, and comparable to the calibration-based algorithms. SIGNIFICANCE: This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.

Source decomposition of the frontocentral auditory steady-state gamma band response in schizophrenia patients and healthy subjects.

AIM: Gamma-band auditory steady-state response (ASSR) is a neurophysiologic index that is increasingly used as a translational biomarker in the development of treatments of neuropsychiatric disorders. While gamma-band ASSR is generated by distributed networks of highly interactive temporal and frontal cortical sources, the majority of human gamma-band ASSR studies using electroencephalography (EEG) highlight activity from only a single frontocentral scalp site, Fz, where responses tend to be largest and reductions in schizophrenia patients are most evident. However, no previous study has characterized the relative source contributions to Fz, which is a necessary step to improve the concordance of preclinical and clinical EEG studies. METHODS: A novel method to back-project the contributions of independent cortical source components was applied to assess the independent sources and their proportional contributions to Fz as well as source-resolved responses in 432 schizophrenia patients and 294 healthy subjects. RESULTS: Independent contributions of gamma-band ASSR to Fz were detected from orbitofrontal, bilateral superior/middle/inferior temporal, bilateral middle frontal, and posterior cingulate gyri in both groups. In contrast to expectations, the groups showed comparable source contribution weight to gamma-band ASSR at Fz. While gamma-band ASSR reductions at Fz were present in schizophrenia patients consistent with previous studies, no group differences in individual source-level responses to Fz were detected. CONCLUSION: Small differences in multiple independent sources summate to produce scalp-level differences at Fz. The identification of independent source contributions to a single scalp sensor represents a promising methodology for measuring dissociable and homologous biomarker targets in future translational studies.

Sources of the frontocentral mismatch negativity and P3a responses in schizophrenia patients and healthy comparison subjects.

BACKGROUND: Mismatch negativity (MMN) and P3a are event-related potential measures of early auditory information processing that are increasingly used as translational biomarkers in the development of treatments for neuropsychiatric disorders. These responses are reduced in schizophrenia patients over the frontocentral scalp electrodes and are associated with important domains of cognitive and psychosocial functioning. While MMN and P3a responses are generated by a dynamic network of cortical sources distributed across the temporal and frontal brain regions, it is not clear how these sources independently contribute to MMN and P3a at the primary frontocentral scalp electrode or to abnormalities observed in schizophrenia. This study aimed to determine the independent source contributions and characterize the magnitude of impairment in source-level MMN and P3a responses in schizophrenia patients. METHODS: A novel method was applied to back-project the contributions of 11 independent cortical source components to Fz, the primary scalp sensor that is used in clinical studies, in n = 589 schizophrenia patients and n = 449 healthy comparison subjects. RESULTS: The groups showed comparable individual source contributions underlying both MMN and P3a responses at Fz. Source-level responses revealed an increasing magnitude of impairment in schizophrenia patients from the temporal to more frontal sources. CONCLUSIONS: Schizophrenia patients have a normal architecture of source contributions that are accompanied by widespread abnormalities in source resolved mismatch and P3a responses, with more prominent deficits detected from the frontal sources. Quantification of source contributions and source-level responses accelerates clarification of the neural networks underlying MMN reduction at Fz in schizophrenia patients.

Boosting template-based SSVEP decoding by cross-domain transfer learning.

Objective. This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) by leveraging cross-domain data transferring.Approach. We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and electroencephalogram montages).Main results. Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method.Significance. This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.

Usefulness and Safety of Remote Continuous Glucose Monitoring for a Severe COVID-19 Patient with Diabetes.

Diabetes is associated with mortality and severity of coronavirus disease (COVID-19). Protecting against infection in health care workers at high risk of COVID-19 is critical. This report investigates the usefulness and safety of remote continuous glucose monitoring (CGM) in a patient with diabetes and severe interstitial pneumonia caused by the coronavirus disease. The Dexcom G4 Platinum CGM system® was used to monitor blood glucose (BG) levels from outside the patient's isolation room. Continuous insulin infusion rates and boluses were determined based on the patient's BG levels. Real-time CGM made it possible to track BG trends and prevent dramatic variations in BG, although the rate of insulin infusion changed dynamic. Furthermore, the need for health care workers to enter the isolation room was minimized because the Dexcom G4 Platinum CGM system can evaluate from a distance of up to 6.0 m.

Statistically Optimized Spatial Filtering in Decoding Steady-State Visual Evoked Potentials Based on Task-Related Component Analysis.

Task-related component analysis (TRCA) has been the most effective spatial filtering method in implementing high-speed brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). TRCA is a data-driven method, in which spatial filters are optimized to maximize inter-trial covariance of time-locked electroencephalographic (EEG) data, formulated as a generalized eigenvalue problem. Although multiple eigenvectors can be obtained by TRCA, the traditional TRCA-based SSVEP detection considered only one that corresponds to the largest eigenvalue to reduce its computational cost. This study proposes using multiple eigen-vectors to classify SSVEPs. Specifically, this study integrates a task consistency test, which statistically identifies whether the component reconstructed by each eigenvector is task-related or not, with the TRCA-based SSVEP detection method. The proposed method was evaluated by using a 12-class SSVEP dataset recorded from 10 subjects. The study results indicated that the task consistency test usually identified and suggested more than one eigenvectors (i.e., spatial filters). Further, the use of additional spatial filters significantly improved the classification accuracy of the TRCA-based SSVEP detection.

Questionable Classification Accuracy Reported in "Designing a Sum of Squared Correlations Framework for Enhancing SSVEP-Based BCIs".

This commentary presents a replication study to verify the effectiveness of a sum of squared correlations (SSCOR)-based steady-state visual evoked potentials (SSVEPs) decoding method proposed by Kumar et al.. We implemented the SSCOR-based method in accordance with their descriptions and estimated its classification accuracy using a benchmark SSVEP dataset with cross validation. Our results showed significantly lower classification accuracy compared with the ones reported in Kumar et al.'s study. We further investigated the sources of performance discrepancy by simulating data leakage between training and test datasets. The classification performance of the simulation was remarkably similar to those reported by Kumar et al.. We, therefore, question the validity of evaluation and conclusions drawn in Kumar et al.'s study.

Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses.

OBJECTIVE: This paper proposes a novel device-to-device transfer-learning algorithm for reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller by leveraging electroencephalographic (EEG) data previously acquired by different EEG systems. METHODS: The transferring is done by projecting the scalp-channel EEG signals onto a shared latent domain across devices. Three spatial filtering techniques, including channel averaging, canonical correlation analysis (CCA), and task-related component analysis (TRCA), were employed to extract the shared responses from different devices. The transferred data were integrated into a template-matching-based algorithm to detect SSVEPs. To evaluate its transferability, this paper conducted two sessions of simulated online BCI experiments with ten subjects using 40 visual stimuli modulated by joint frequency-phase coding method. In each session, two different EEG devices were used: first, the Quick-30 system (Cognionics, Inc.) with dry electrodes, and second, the ActiveTwo system (BioSemi, Inc.) with wet electrodes. RESULTS: The proposed method with CCA- and TRCA-based spatial filters achieved significantly higher classification accuracy compared with the calibration-free standard CCA-based method. CONCLUSION: This paper validated the feasibility and effectiveness of the proposed method in implementing calibration-free SSVEP-based BCIs. SIGNIFICANCE: The proposed method has great potentials to enhance practicability and usability of real-world SSVEP-based BCI applications by leveraging user-specific data recorded in previous sessions even with different EEG systems and montages.

[A Case of Aortic Graft Infection Due to Listeria monocytogenes Detected by Microarray-Based, Multiplexed, Automated Molecular Diagnosis System].

A 80-year-old man was transferred to our hospital for hemoptysis caused by erosion(perforation) of thoracic aortic stent graft infection into the airway. Blood cultures on admission detected Gram-positive rods, and a microarray-based, multiplexed, automated molecular diagnosis instrument (Verigene® system) identified Listeria spp. Although Listeria monocytogenes is rare organism of stent graft infection, we were able to start appropriate antibiotic therapy on the second hospital day due to rapid identification of bacteria. Verigene® system is considered to be useful in severe infectious diseases including stent graft infections, even if the causative organism is rare.

EEG-Based Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) provide a direct communication channel between human brain and output devices. Due to advantages such as non-invasiveness, ease of use, and low cost, electroencephalography (EEG) is the most popular method for current BCIs. This chapter gives an overview of the current EEG-based BCIs for the main purpose of communication and control. This chapter first provides a taxonomy of the EEG-based BCI systems by categorizing them into three major groups: (1) BCIs based on event-related potentials (ERPs), (2) BCIs based on sensorimotor rhythms, and (3) hybrid BCIs. Next, this chapter describes challenges and potential solutions in developing practical BCI systems toward high communication speed, convenient system use, and low user variation. Then this chapter briefly reviews both medical and non-medical applications of current BCIs. Finally, this chapter concludes with a summary of current stage and future perspectives of the EEG-based BCI technology.

Robustness analysis of decoding SSVEPs in humans with head movements using a moving visual flicker.

OBJECTIVE: The emergence of mobile electroencephalogram (EEG) platforms have expanded the use cases of brain-computer interfaces (BCIs) from laboratory-oriented experiments to our daily life. In challenging situations where humans' natural behaviors such as head movements are unrestrained, various artifacts could deteriorate the performance of BCI applications. This paper explored the effect of muscular artifacts generated by participants' head movements on the signal characteristics and classification performance of steady-state visual evoked potentials (SSVEPs). APPROACH: A moving visual flicker was employed to induce not only SSVEPs but also horizontal and vertical head movements at controlled speeds, leading to acquiring EEG signals with intensity-manipulated muscular artifacts. To properly induce neck muscular activities, a laser light was attached to participants' heads to give visual feedback; the laser light indicates the direction of the head independently from eye movements. The visual stimulus was also modulated by four distinct frequencies (10, 11, 12, and 13 Hz). The amplitude and signal-to-noise ratio (SNR) were estimated to quantify the effects of head movements on the signal characteristics of the elicited SSVEPs. The frequency identification accuracy was also estimated by using well-established decoding algorithms including calibration-free and fully-calibrated approaches. MAIN RESULTS: The amplitude and SNR of SSVEPs tended to deteriorate when the participants moved their heads, and this tendency was significantly stronger in the vertical head movements than in the horizontal movements. The frequency identification accuracy also deteriorated in proportion to the speed of head movements. Importantly, the accuracy was significantly higher than its chance-level regardless of the level of artifact contamination and algorithms. SIGNIFICANCE: The results suggested the feasibility of decoding SSVEPs in humans freely moving their head directions, facilitating the real-world applications of mobile BCIs.

Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles.

Muscular artifacts often contaminate electroencephalograms (EEGs) and deteriorate the performance of brain-computer interfaces (BCIs). Although many artifact reduction techniques are available, most of the studies have focused on their reduction ability (i.e. reconstruction errors), and it has been missing to evaluate their effect on the performance of BCIs. This study aims at evaluating the performance of a state-of-the-art muscular artifact reduction technique on a scenario of a steady-state visual evoked potentials (SSVEPs)based BCI. The performance was evaluated based on a semisimulation setting using a benchmark dataset of SSVEPs artificially contaminated by muscular artifacts acquired from the trapezius. Our results showed that combining the artifact reduction method and the classification algorithm based on the task-related component analysis gained improved classification accuracy. Interestingly, the artifact reduction setting minimizing the reconstruction errors, i.e. elaborately recovering the true EEG waveforms, was inconsistent to the one maximizing the classification performance. The results suggest that artifact reduction methods should be tuned so as to tomaximize performance of BCIs.