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. However, it remains unclear if the advanced decoding algorithms can extract highfidelity SSVEPs from the non-hair EEG and enhance the practicality of non-hair BCIs in real-world environments. This study aims to quantitatively evaluate whether, and if so, to what extent the non-hair BCIs can leverage the state-of-art decoding algorithms. Eleven healthy individuals participated in a 5-target SSVEP BCI experiment. A high-density EEG cap recorded SSVEPs from both hair-covered and non-hair-bearing regions. By evaluating and demonstrating the accessibility of nonhair-bearing behind-ear signals, our assessment characterized constraints on data length, trial numbers, channels, and their relationships with the decoding algorithms, providing practical guidelines to optimize SSVEP-based BCI systems in real-life applications.

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. The proposed method employs spatial filtering techniques including response averaging, canonical correlation analysis (CCA), and task- related component analysis (TRCA) to project scalp EEG recordings onto a shared response domain. The transferability was evaluated by using 40-class SSVEPs recorded from eight subjects with nine electrodes on two different days. Three subsets of electrode montages were selected to simulate different scenarios such as identical, partly overlapped, and non-overlapped electrode placements across two sessions. The target identification accuracy of the proposed methods with transferred training data significantly outperformed a conventional training-free algorithm. The result suggests training data required in the BCI speller could be transferred from different EEG montages and/or headsets.

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.

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.

Cathepsin K inhibitor causes changes in crystallinity and crystal structure of newly-formed mandibular bone in rats

Cathepsin K inhibitors are new drugs with the potential for the treatment of osteoporosis because they sustain bony remodelling better than bone resorption inhibitors such as bisphosphonates. The treatment of osteoporosis with inhibitors of bony resorption is associated with osteonecrosis of the jaw, as the deterioration in bony quality that they induce is thought to be one of its causes. The quality of bone is delineated by structural and material characteristics (which include the degree and quality of mineralisation, and depends on the content of proteoglycan and the structural integrity of the bony collagen).^1,2 Animal and clinical studies have shown that cathepsin K inhibitors improve the mineral density and structural characteristics of bone, but their effect on the rest remains unknown. We therefore hypothesised that these inhibitors will affect the material characteristics of newly-formed mandibular bone. To verify our hypothesis, we used Raman microspectroscopy to examine such bone in rats that were given a cathepsin K inhibitor, and found unusual crystallinity and an increased substitution of carbonate (CO₃^2-) in its crystal structure.

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.com/mnakanishi/TRCA-SSVEP) for implementing TRCA method provided by Dr. Masaki Nakanishi, the first author of [2], were used to take the role of the TRCA method. At that time, the proposed CORCA-based method outperforms the TRCA-based method [1].

A dynamic stopping method for improving performance of steady-state visual evoked potential based brain-computer interfaces

The performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been drastically improved in the past few years. In conventional SSVEP-based BCIs, the speed of a selection is fixed towards high performance based on preliminary offline analysis. However, due to inter-trial variability, the optimal selection time to achieve sufficient accuracy is different for each trial. To optimize the performance of SSVEP-based BCIs, this study proposed a dynamic stopping method that can adaptively determine a selection time in each trial by applying a threshold to the probability of detecting a target. A 12-class SSVEP dataset recorded from 10 subjects was used to evaluate the performance of the proposed method. Compared to the conventional method with a fixed selection time towards the highest accuracy, the proposed method could significantly reduce the averaged selection time (0.84±0.39 s vs. 1.44±0.63 s, p<;0.05) with comparable accuracy (99.44±1.57 % vs. 99.55±1.22 %). As a result, the simulated online information transfer rate (ITR) with the dynamic stopping method achieved a significant improvement compared to the conventional method (125.30±21.55 bits/min vs. 92.75±23.77 bits/min). These results suggest that the proposed dynamic stopping method is effective for improving the performance of SSVEP-based BCI systems.

Molecular alterations of newly formed mandibular bone caused by zoledronate

Bone quality is defined by structural and material characteristics. Most studies on the mandible have focused on the analysis of structural characteristics, with insufficient investigation of material characteristics. This study tested whether zoledronate affects the material characteristics of newly formed mandibular bone. Thirty-six female Wistar rats were assigned to three groups: sham-ovariectomized rats (SHAM, n=12), ovariectomized rats (OVX, n=12), and ovariectomized rats treated with zoledronate (ZOL, n=12). The left side of the mandibular ramus of all rats was drilled bicortically. Twenty-eight days after surgery, all surviving rats were euthanized and all mandibles were removed. Raman microspectroscopy was performed, and five spectra per specimen of newly formed mandibular bone were analysed. Compared with OVX rats, the mineral/matrix ratio in ZOL rats was significantly increased (5.43±1.88 vs. 7.86±2.05), while crystallinity (0.055±0.002 vs. 0.050±0.002), relative proteoglycan content (0.43±0.10 vs. 0.31±0.05), and collagen structural integrity (1.16±0.21 vs. 0.72±0.06) were significantly decreased. These changes in material characteristics may explain why rats that received zoledronate exhibited peculiar biological phenomena such as bisphosphonate-related osteonecrosis of the jaw.

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis

OBJECTIVE

This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller.

METHODS

Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects.

RESULTS

The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task.

CONCLUSION

This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI.

SIGNIFICANCE

The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.

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. This study proposes to apply the ICA-based spatial filters to test data and individual templates obtained by averaging training trials, and then to use the correlation coefficients between the filtered data and templates as features for SSVEP classification. This study applied the proposed method to a 40-class SSVEP dataset to evaluate its classification accuracy against those obtained by conventional canonical correlation analysis (CCA)- and extended CCA-based methods. The study results showed that the ICA-based method outperformed the other methods in terms of the classification accuracy. Furthermore, its computational time was comparable to the CCA-based method, and was much shorter than that of the extended CCA-based method.

Clonal structure in Ichthyobacterium seriolicida, the causative agent of bacterial haemolytic jaundice in yellowtail, Seriola quinqueradiata, inferred from molecular epidemiological analysis

Bacterial haemolytic jaundice caused by Ichthyobacterium seriolicida has been responsible for mortality in farmed yellowtail, Seriola quinqueradiata, in western Japan since the 1980s. In this study, polymorphic analysis of I. seriolicida was performed using three molecular methods: amplified fragment length polymorphism (AFLP) analysis, multilocus sequence typing (MLST) and multiple-locus variable-number tandem repeat analysis (MLVA). Twenty-eight isolates were analysed using AFLP, while 31 isolates were examined by MLST and MLVA. No polymorphisms were identified by AFLP analysis using EcoRI and MseI, or by MLST of internal fragments of eight housekeeping genes. However, MLVA revealed variation in repeat numbers of three elements, allowing separation of the isolates into 16 sequence types. The unweighted pair group method using arithmetic averages cluster analysis of the MLVA data identified four major clusters, and all isolates belonged to clonal complexes. It is likely that I. seriolicida populations share a common ancestor, which may be a recently introduced strain.

Stim1 Regulates Enamel Mineralization and Ameloblast Modulation

Loss-of-function mutations in the Ca²⁺ release-activated Ca²⁺ channel genes ORAI1 and STIM1 abolish store-operated Ca²⁺ entry (SOCE) and result in ectodermal dysplasia with amelogenesis imperfecta. However, because of the limited availability of patient tissue, analyses of enamel mineralization or possible changes in ameloblast function or morphology have not been possible. Here, we generated mice with ectodermal tissue-specific deletion of Stim1 ( Stim1 cKO [conditional knockout]), Stim2 ( Stim2 cKO), and Stim1 and Stim2 ( Stim1/2 cKO) and analyzed their enamel phenotypes as compared with those of control ( Stim1/2^fl/fl) animals. Ablation of Stim1 and Stim1/2 but not Stim2 expression resulted in chalky enamel and severe attrition at the incisor tips and molar cusps. Stim1 and Stim1/2 cKO, but not Stim2 cKO, demonstrated inferior enamel mineralization with impaired structural integrity, whereas the shape of the teeth and enamel thickness appeared to be normal in all animals. The gene expression levels of the enamel matrix proteins Amelx and Ambn and the enamel matrix proteases Mmp20 and Klk4 were not altered by the abrogation of SOCE in Stim1/2 cKO mice. The morphology of ameloblasts during the secretory and maturation stages was not significantly altered in either the incisors or molars of the cKO animals. However, in Stim1 and Stim1/2 cKO incisors, the alternating modulation of maturation-stage ameloblasts between the smooth- and ruffle-ended cell types continued beyond the regular cycle and extended to the areas corresponding to the zone of postmodulation ameloblasts in the teeth of control animals. These results indicate that SOCE is essential for proper enamel mineralization, in which Stim1 plays a critical role during the maturation process.

Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss

Importance

The current assessment of visual field loss in diseases such as glaucoma is affected by the subjectivity of patient responses and the lack of portability of standard perimeters.

Objective

To describe the development and initial validation of a portable brain-computer interface (BCI) for objectively assessing visual function loss.

Design, Setting, and Participants

This case-control study involved 62 eyes of 33 patients with glaucoma and 30 eyes of 17 healthy participants. Glaucoma was diagnosed based on a masked grading of optic disc stereophotographs. All participants underwent testing with a BCI device and standard automated perimetry (SAP) within 3 months. The BCI device integrates wearable, wireless, dry electroencephalogram and electrooculogram systems and a cellphone-based head-mounted display to enable the detection of multifocal steady state visual-evoked potentials associated with visual field stimulation. The performances of global and sectoral multifocal steady state visual-evoked potentials metrics to discriminate glaucomatous from healthy eyes were compared with global and sectoral SAP parameters. The repeatability of the BCI device measurements was assessed by collecting results of repeated testing in 20 eyes of 10 participants with glaucoma for 3 sessions of measurements separated by weekly intervals.

Main Outcomes and Measures

Receiver operating characteristic curves summarizing diagnostic accuracy. Intraclass correlation coefficients and coefficients of variation for assessing repeatability.

Results

Among the 33 participants with glaucoma, 19 (58%) were white, 12 (36%) were black, and 2 (6%) were Asian, while among the 17 participants with healthy eyes, 9 (53%) were white, 8 (47%) were black, and none were Asian. The receiver operating characteristic curve area for the global BCI multifocal steady state visual-evoked potentials parameter was 0.92 (95% CI, 0.86-0.96), which was larger than for SAP mean deviation (area under the curve, 0.81; 95% CI, 0.72-0.90), SAP mean sensitivity (area under the curve, 0.80; 95% CI, 0.69-0.88; P = .03), and SAP pattern standard deviation (area under the curve, 0.77; 95% CI, 0.66-0.87; P = .01). No statistically significant differences were seen for the sectoral measurements between the BCI and SAP. Intraclass coefficients for global and sectoral parameters ranged from 0.74 to 0.92, and mean coefficients of variation ranged from 3.03% to 7.45%.

Conclusions and Relevance

The BCI device may be useful for assessing the electrical brain responses associated with visual field stimulation. The device discriminated eyes with glaucomatous neuropathy from healthy eyes in a clinically based setting. Further studies should investigate the feasibility of the BCI device for home-based testing as well as for detecting visual function loss over time.

[Selection of Laboratory Procedures to Detect Toxigenic by the 2-Step Method]

The 2-step method is an algorithm to detect toxigenic Clostridium difficile. We herein compared the sensitivities and specificities of an enzyme immunoassay (toxin A/B-EIA), toxigenic culture (TC-EIA), Loop-Mediated Isothermal Amplification assay (LAMP), and Xpert C. difficile (Xpert) with the detection of the toxin B gene by a polymerase chain reaction (PCR). The results obtained showed that the sensitivities and specificities of toxin A/B-EIA, Xpert, TC-EIA, and LAMP were 30 and 100%, 87.2 and 100%, 97.5 and 89.7%, and 95 and 100%, respectively. We also evaluated the turnaround time (TAT) and cost of toxigenic C. difficile detection. Our hospital TAT for toxin A/B-EIA and TC-EIA are 37 min and 5 days, respectively. We estimated the TAT of Xpert, LAMP, and PCR to be 105 min, 5 days, and 6 days, respectively. On the other hand, the cost to detect toxigenic C. difficile increased in the order of TC-EIA, LAMP, Xpert, and PCR. We have never experienced outbreak of Clostridium difficile infection (CDI) in our hospital, and there is less the number of CDI than other place. So we selected TC-EIA that is good sensitivity and low cost per specimen. Hereafter it'll be necessary to solve a problem it takes time, because we have to respond to outbreak of CDI quickly if it happens.