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.

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.

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.

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.

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.

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.

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.

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

. 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..

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.

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.

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.

[A Case of Aortic Graft Infection Due to 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 spp. Although 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.

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.

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).

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.

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.

Evaluating the Performance of Non-Hair SSVEP-Based BCIs Featuring Template-Based Decoding Methods.

Our previous study has demonstrated the feasibility of employing non-hair-bearing electrodes to build a Steadystate Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, relaxing technical barriers in preparation time and offering an ease-of-use apparatus. The signal quality of the SSVEPs and the resultant performance of the non-hair BCI, however, did not close upon those reported in the state-of-the-art BCI studies based on the electroencephalogram (EEG) measured from the occipital regions. Recently, advanced decoding algorithms such as task-related component analysis have made a breakthrough in enhancing the signal quality of the occipital SSVEPs and the performance of SSVEP-based BCIs in a well-controlled laboratory environment.

Transferring Shared Responses Across Electrode Montages for Facilitating Calibration in High-Speed Brain Spellers.

Recent studies have shown that using the user's average steady-state visual evoked responses (SSVEPs) as the template to template-matching methods could significantly improve the accuracy and speed of the SSVEP-based brain- computer interface (BCI). However, collecting the pilot data for each individual can be time-consuming. To resolve this practical issue, this study aims to explore the feasibility of leveraging pre- recorded datasets from the same users by transferring common electroencephalogram (EEG) responses across different sessions with the same or different electrode montages.

Correction to "Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface".

In the above paper [1], a method has been proposed to use the correlated component analysis (CORCA) to learn spatial filters with multiple blocks of individual training data for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) scenario. In order to evaluate the performance of CORCA, the task-related component analysis (TRCA)-based method was used as a baseline method [2]. For a fair and convincing comparison, the MATLAB codes on the website (https://github.

Independent component analysis-based spatial filtering improves template-based SSVEP detection.

This study proposes a new algorithm to detect steady-state visual evoked potentials (SSVEPs) based on a template-matching approach combined with independent component analysis (ICA)-based spatial filtering. In recent studies, the effectiveness of the template-based SSVEP detection has been demonstrated in a high-speed brain-computer interface (BCI). Since SSVEPs can be considered as electroencephalogram (EEG) signals generated from underlying brain sources independent from other activities and artifacts, ICA has great potential to enhance the signal-to-noise ratio (SNR) of SSVEPs by separating them from artifacts.