Can vibrotactile stimulation and tDCS help inefficient BCI users?

Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation's (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter's BCI performance in the vibrotactile stimulation group increased significantly by 9.13% (p < 0.01), and while the tDCS group subjects' performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking values (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers showed no significant stimulation effects across all groups. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users.

Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI.

Brain-computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

A host target of a bacterial cysteine protease virulence effector plays a key role in convergent evolution of plant innate immune system receptors.

Some virulence effectors secreted from pathogens target host proteins and induce biochemical modifications that are monitored by nucleotide-binding and leucine-rich repeat (NLR) immune receptors. Arabidopsis RIN4 protein (AtRIN4: RPM1-interacting protein 4) homologs are present in diverse plant species and targeted by several bacterial type III effector proteins including the cysteine protease AvrRpt2. RIN4 is 'guarded' by several independently evolved NLRs from various plant species, including Arabidopsis RPS2. Recently, it was shown that the MR5 NLR from a wild apple relative can recognize the AvrRpt2 effector from Erwinia amylovora, but the details of this recognition remained unclear. The present contribution reports the mechanism of AvrRpt2 recognition by independently evolved NLRs, MR5 from apple and RPS2, both of which require proteolytically processed RIN4 for activation. It shows that the C-terminal cleaved product of apple RIN4 (MdRIN4) but not AtRIN4 is necessary and sufficient for MR5 activation. Additionally, two polymorphic residues in AtRIN4 and MdRIN4 are identified that are crucial in the regulation of and physical association with NLRs. It is proposed that polymorphisms in RIN4 from distantly related plant species allow it to remain an effector target while maintaining compatibility with multiple NLRs.

High Contiguity Whole Genome Sequence and Gene Annotation Resource for Two Venturia nashicola Isolates.

Venturia nashicola is a fungal pathogen that causes Asian pear scab disease. This pathogen is of particular importance in Northeast Asian countries, where Asian pears are grown industrially. Scab disease in Asian pear is currently controlled by fungicide spraying and this situation calls for developing scab resistant cultivars. High-quality genome data are therefore required for in-depth comparative genome analysis of different isolates of V. nashicola and V. pyrina, a closely related species, which only infects European pear plants. Here, we report the high-contiguity whole genome assembly of two V. nashicola isolates, which is expected to enable genome comparisons for identification of the genes involved in host range determination of V. nashicola.

Whole Genome Sequence Resource of the Asian Pear Scab Pathogen Venturia nashicola.

Venturia nashicola, the cause of scab disease of Asian pears, is a host-specific, biotrophic fungus. It is restricted to Asia and is regarded as a quarantine threat outside this region. European pear displays nonhost resistance (NHR) to V. nashicola and Asian pears are nonhosts of V. pyrina (the cause of European pear scab disease). The host specificity of these two fungi is likely governed by differences in their effector arsenals, with a subset hypothesized to activate NHR. The Pyrus-Venturia pathosystem provides an opportunity to dissect the underlying genetics of nonhost interactions in this potentially more durable form of resistance. The V. nashicola genome will enable comparisons to other Venturia spp. genomes to identify effectors that potentially activate NHR in the pear scab pathosystem.

Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset.

Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62 ± 02.06%, specificity of 76.50 ± 23.50%, and accuracy of 75.59 ± 10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.

Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli.

Objective.Presence is an important aspect of user experience in virtual reality (VR). It corresponds to the illusion of being physically located in a virtual environment (VE). This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in VR.Approach.In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence, this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical VE.Main results.Through analysing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e. the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions.Significance.Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the VE.

Review of public motor imagery and execution datasets in brain-computer interfaces.

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.

Pain Classification using Evoked EEG Induced by Thermal Grill Illusion - Deep Neural Network Approach.

As the quantification of pain has emerged in biomedical engineering today, studies have been developing biomarkers associated with pain actively by measuring bio-signals such as electroencephalogram (EEG). Recently, some EEG studies of cold and hot pain have been reported. However, they used one type of stimulus condition for each trial and a relatively long stimulation time to collect EEG features. In this study, EEG signals during Cool (20 °C), Warm (40 °C), and Thermal Grill Illusion (TGI, 20-40 °C) stimuli were collected from 43 subjects, and were classified by a deep convolutional neural network referred to as EEGNet. Three binary classifications for the three conditions (TGI, Cool, Warm) were conducted for each subject individually. Classification accuracies for TGI-Cool, TGI-Warm, and Warm-Cool were 0.74±0.01, 0.71±0.01, and 0.74±0.01, respectively. For subjects who rated the TGI significantly hotter than the Warm stimulus, the classification accuracy for TGI-Cool (0.74±0.01) was significantly higher than for TGI-Warm (0.71±0.01). In contrast, the classification accuracy for TGI-Cool (0.72±0.03) did not differ statistically from TGI-Warm (0.73±0.01) in subjects without illusion. We found that the TGI and Cool stimuli were classified better than the TGI and Warm stimuli, implying that objective EEG features are consistent with subjective behavioral results. Further, we observed that most discriminative features between the TGI and the Cool or Warm conditions appeared in the parietal area for subjects who perceived the illusion. We postulate that the somato-sensory cortex may be activated when TGI is perceived to be hot pain.

Corrigendum: Review of public motor imagery and execution datasets in brain-computer interfaces.

[This corrects the article DOI: 10.3389/fnhum.2023.1134869.].

An Alternative Method to Evaluate Resistance to Pear Scab (Venturia nashicola).

Two pear cultivars with different degrees of resistance to Venturia nashicola were evaluated on the basis of a disease severity rating for pear scab resistance under controlled environmental condition. Two inoculation techniques were tested: the procedure for inoculation by dropping conidia suspension of V. nashicola; the procedure by deposition of agar plug on the abaxial surface of pear leaves. All tested cultivars resulted in blight symptoms on the inoculated leaves and became spread to uninoculated region or other leaves. Although both methods provide satisfactory infection of V. nashicola on pear leaves, the mycelial plug method of inoculation was more reliable than the spray inoculation method for the evaluation of pear scab disease resistance. The incubation period of V. nashicola in the resistant pear cultivar, Greensis was longer than that in the susceptible cultivar, Hwasan.

EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces.

As attention to deep learning techniques has grown, many researchers have attempted to develop ready-to-go brain-computer interfaces (BCIs) that include automatic processing pipelines. However, to do so, a large and clear dataset is essential to increase the model's reliability and performance. Accordingly, our electroencephalogram (EEG) dataset for rapid serial visual representation (RSVP) and P300 speller may contribute to increasing such BCI research. We validated our dataset with respect to features and accuracy. For the RSVP, the participants (N = 50) achieved about 92% mean target detection accuracy. At the feature level, we observed notable ERPs (at 315 ms in the RSVP; at 262 ms in the P300 speller) during target events compared to non-target events. Regarding P300 speller performance, the participants (N = 55) achieved about 92% mean accuracy. In addition, P300 speller performance over trial repetitions up to 15 was explored. The presented dataset could potentially improve P300 speller applications. Further, it may be used to evaluate feature extraction and classification algorithm effectively, such as for cross-subjects/cross-datasets, and even for the cross-paradigm BCI model.

Whole Genome Enabled Phylogenetic and Secretome Analyses of Two Venturia nashicola Isolates.

Venturia nashicola is a fungal pathogen causing scab disease in Asian pears. It is particularly important in the Northeast Asia region where Asian pears are intensively grown. Venturia nashicola causes disease in Asian pear but not in European pear. Due to the highly restricted host range of Venturia nashicola, it is hypothesized that the small secreted proteins deployed by the pathogen are responsible for the host determination. Here we report the whole genome based phylogenetic analysis and predicted secretomes for V. nashicola isolates. We believe that our data will provide a valuable information for further validation and functional characterization of host determinants in V. nashicola.

Loop-Mediated Isothermal Amplification (LAMP)-Based Turn on Fluorescent Paper (ToFP) Device for Detecting Rosellinia necatrix.

White root rot (WRR) disease caused by Rosellinia necatrix, a fungal pathogen, results in severe damage to various fruit trees, decreasing their marketability. Regular monitoring is a major process because the pathogen can remain in the soil around the host for a long time. Loop-mediated isothermal amplification (LAMP) is a highly sensitive and efficient amplification technology of nucleic acids (DNA or RNA) that can be performed at constant temperatures. Thus, it has been spotlighted as a useful tool for detecting several infectious agents. In the present study, LAMP-based Turn-on Fluorescent Paper (ToFP) devices were designed and applied to detect R. necatrix. LAMP conditions were optimized and found to be optimal at a reaction temperature (62 °C) and a reaction time (30 minutes). These reaction conditions were confirmed by applying them to infectious soil samples collected from the field. The limitation of detection was identified as 10 fg of genomic DNA under optimized LAMP conditions. These LAMP-based ToFP devices were generated with easily available stationery materials and the utility of these devices to analyze the LAMP results were confirmed through several experiments on a total of 14 field samples. The results showed that the developed LAMP-based detection system was very sensitive and had the advantages of rapid detection and high availability in the field.

CANet: A Channel Attention Network to Determine Informative Multi-channel for Image Classification from Brain Signals.

Changes in brain state that depend on various visual image stimulations have been investigated recently; however, it is difficult to decode visual image information from brain signal information. Recently, deep learning techniques have been applied to classify brain signals in various experiments, such as motor imagery and steady state visual evoked potential. However, although the deep learning model seems powerful, it is understood poorly, and thus, can be considered a black box. Accordingly, when multi-channel brain signals are trained, which channels include important information is not understood clearly. In this paper, we proposed a channel attention network (CANet) and investigated the way the deep learning network may determine which channels contain more important information that represents brainwaves' characteristics and the way it may visualize that information. Using such spatial channel information, we found that our proposed deep learning architecture outperforms basic approaches (spatial channel information is not considered) to classifying categorized images from visual evoked magnetoencephalographic (MEG) brain signals.

P300 Speller Performance Predictor Based on RSVP Multi-feature.

Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller's performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller's performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300's speller performance. We found that several of the RSVP's event-related potential (ERP) and behavioral features were correlated with the P300 speller's offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor (r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor (r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone.

The Neurophysiological Effect of Acoustic Stimulation with Real-time Sleep Spindle Detection.

Sleep spindle is a salient brain activity found in the sigma frequency range (11-16 Hz) during sleep stage 2. It has been demonstrated that sleep spindle is related to memory consolidation, neurodegenerative disease, and mental disorders. Slow wave activity (0.5-4 Hz) is the most prominent EEG activity during sleep and appears as a large, spontaneous synchronization of cortical neurons. The role of slow wave activity has been proposed to regulate synaptic strength and memory consolidation. Many studies have investigated the effect of acoustic stimuli during the sleep slow wave. However, there have been few studies which investigated an effect of acoustic stimulation during sleep spindle activity. In this study, we examined the neurophysiological effect of acoustic stimulation during sleep spindle activity. We delivered pink noise after the detection of sleep spindle, and surmised that acoustic stimulation after sleep spindle detection may preserve delta activity during ongoing sleep. Further, we observed suppression of the sleep spindle activity around the times of acoustic stimulation and evoked slow wave activity and theta band activity immediately after tone onset.

Aptamer-Based Pathogen Monitoring for Salmonella enterica ser. Typhimurium.

Salmonella enterica ser. Typhimurium is a foodborne pathogen that causes salmonellosis. Symptoms of salmonellosis include fever, diarrhea, and gastroenteritis. Conventional culture methods for detecting foodborne bacterial pathogens require long incubation time, expensive immunoassay methods, and sample enrichment steps. The objective of this study was to develop an aptamer-based sandwich assay to detect S. enterica ser. Typhimurium. First, S. enterica ser. Typhimurium specific binding aptamers (S11 and S24) were obtained by whole-cell SELEX for high sensitivity and specificity detection using live S. enterica ser. Typhimurium. S11 and S24 aptamers were able to capture S. enterica ser. Typhimurium selectively and distinguish it from other species of Salmonella (S. enterica ser. Typhimurium, S. enterica ser. Choleraesuis, S. enterica ser. Dublin, and S. enterica ser. Enteritidis) and food-borne bacterial pathogens (Escherichia coli K12, Listeria monocytogenes, Shigella sonnei and Staphylococcus aureus) with KD values of 4.41×10-12 M and 3.75×10-11 M, respectively. S. enterica ser. Typhimurium. aptamer-based sandwich assay exhibited a linear response for sensing S. enterica ser. Typhimurium. cells at concentration ranging from 2×101 to 2×105 CFU/mL. This aptamer-based sandwich assay can be used for on-site detection of S. enterica ser. Typhimurium.

Characterization of the Rosellinia necatrix Transcriptome and Genes Related to Pathogenesis by Single-Molecule mRNA Sequencing.

White root rot disease, caused by the pathogen Rosellinia necatrix, is one of the world's most devastating plant fungal diseases and affects several commercially important species of fruit trees and crops. Recent global outbreaks of R. necatrix and advances in molecular techniques have both increased interest in this pathogen. However, the lack of information regarding the genomic structure and transcriptome of R. necatrix has been a barrier to the progress of functional genomic research and the control of this harmful pathogen. Here, we identified 10,616 novel full-length transcripts from the filamentous hyphal tissue of R. necatrix (KACC 40445 strain) using PacBio single-molecule sequencing technology. After annotation of the unigene sets, we selected 14 cell cycle-related genes, which are likely either positively or negatively involved in hyphal growth by cell cycle control. The expression of the selected genes was further compared between two strains that displayed different growth rates on nutritional media. Furthermore, we predicted pathogen-related effector genes and cell wall-degrading enzymes from the annotated gene sets. These results provide the most comprehensive transcriptomal resources for R. necatrix, and could facilitate functional genomics and further analyses of this important phytopathogen.

The draft genome sequence of European pear (Pyrus communis L. 'Bartlett').

We present a draft assembly of the genome of European pear (Pyrus communis) 'Bartlett'. Our assembly was developed employing second generation sequencing technology (Roche 454), from single-end, 2 kb, and 7 kb insert paired-end reads using Newbler (version 2.7). It contains 142,083 scaffolds greater than 499 bases (maximum scaffold length of 1.2 Mb) and covers a total of 577.3 Mb, representing most of the expected 600 Mb Pyrus genome. A total of 829,823 putative single nucleotide polymorphisms (SNPs) were detected using re-sequencing of 'Louise Bonne de Jersey' and 'Old Home'. A total of 2,279 genetically mapped SNP markers anchor 171 Mb of the assembled genome. Ab initio gene prediction combined with prediction based on homology searching detected 43,419 putative gene models. Of these, 1219 proteins (556 clusters) are unique to European pear compared to 12 other sequenced plant genomes. Analysis of the expansin gene family provided an example of the quality of the gene prediction and an insight into the relationships among one class of cell wall related genes that control fruit softening in both European pear and apple (Malus × domestica). The 'Bartlett' genome assembly v1.0 (http://www.rosaceae.org/species/pyrus/pyrus_communis/genome_v1.0) is an invaluable tool for identifying the genetic control of key horticultural traits in pear and will enable the wide application of marker-assisted and genomic selection that will enhance the speed and efficiency of pear cultivar development.