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A frequency range exceeding approximately 30 Hz, denoted as the gamma frequency range, is associated with various cognitive functions, consciousness, sensory integration, short-term memory, working memory, encoding and maintenance of episodic memory, and retrieval processes. In this study, we proposed a new form of gamma stimulation, called gamma music, combining 40 Hz auditory stimuli and music. This gamma music consists of drums, bass, and keyboard sounds, each containing a 40 Hz frequency oscillation. Since 40 Hz stimuli are known to induce an auditory steady-state response (ASSR), we used the 40 Hz power and phase locking index (PLI) as indices of neural activity during sound stimulation. We also recorded subjective ratings of each sound through a questionnaire using a visual analog scale. The gamma music, gamma drums, gamma bass, and gamma keyboard sounds showed significantly higher values in 40 Hz power and PLI compared to the control music without a 40 Hz oscillation. Particularly, the gamma keyboard sound showed a potential to induce strong ASSR, showing high values in these indices. In the subjective ratings, the gamma music, especially the gamma keyboard sound, received more relaxed, comfortable, preferred, pleasant, and natural impressions compared to the control music with conventional gamma stimulation. These results indicate that our proposed gamma music has potential as a new method for inducing ASSR. Particularly, the gamma keyboard sound proved to be an effective acoustic source for inducing a strong ASSR while preserving the comfortable and pleasant sensation of listening to music. Our developed gamma music, characterized by its pleasantness to the human ear, offers a significant advantage for the long-term use of gamma stimulation. The utilization of this music could potentially reduce the physical and psychological burden on participants compared to conventional 40 Hz stimuli. This music is not only expected to contribute to fundamental neuroscience research utilizing ASSR but also to facilitate the implementation of gamma music-based interventions aimed at enhancing human cognitive functions in everyday life.

The mechanical parameters of keyboard switches affect the psychological sense of pressing. The effects of different mechanical parameters on psychological sense have been quantified using questionnaires, but these subjective evaluations are unable to fully clarify the modulation of information processing in the brain due to these differences. This study aimed to elucidate the ability of electroencephalography (EEG) measurements to detect the modulation of subconscious information processing according to mechanical parameter values. To this end, we prepared five mechanical switches with linearly increasing values of pretravel (PT: the distance from the free position until the operating position). We hypothesized that the differences in PTs would subconsciously affect the motor preparation prior to pressing switches because switches with PTs that deviated from those commonly used were predicted to increase the users' attention level when pressing. Differences in motor preparation were quantified using the mean amplitudes of the late contingent negative variation (CNV). We recorded EEGs of 25 gamers during a reaction task for fast switch pressing after a response cue preceded by a pre-cue for response preparation; we also measured the reaction time feedback on each switch pressing trial. Participants performed five sessions (60 trials per session) in total. For the analysis, trials were divided into first (session 1, 2, and 3) and second half sessions (session 4 and 5). In the latter session, CNV amplitudes were significantly higher for the switch with the highest PT than for that with a medium PT, which is closest to that commonly used in commercial mechanical switches. On the other hand, the questionnaire did not detect any significant differences between PTs in their subjective rankings of the psychological effects of switch pressing. These results suggest that differences in PTs modulate motor preparation to press switches, and that EEG measurements may provide a novel objective evaluation of the mechanical parameters of keyboard switches.

This study had two goals: to clarify the relationship between electroencephalographic (EEG) features estimated while non-native speakers listened to a second language (L2) and their proficiency in L2 determined by a conventional paper test and to provide a predictive model for L2 proficiency based on EEG features. We measured EEG signals from 205 native Japanese speakers, who varied widely in English proficiency while they listened to natural speech in English. Following the EEG measurement, they completed a conventional English listening test for Japanese speakers. We estimated multivariate temporal response functions separately for word class, speech rate, word position, and parts of speech. We found significant negative correlations between listening score and 17 EEG features, which included peak latency of early components (corresponding to N1 and P2) for both open and closed class words and peak latency and amplitude of a late component (corresponding to N400) for open class words. On the basis of the EEG features, we generated a predictive model for Japanese speakers’ English listening proficiency. The correlation coefficient between the true and predicted listening scores was 0.51. Our results suggest that L2 or foreign language ability can be assessed using neural signatures measured while listening to natural speech, without the need of a conventional paper test.

Feedback outcomes are generally classified into positive and negative feedback. People often predict a feedback outcome with information that is based on both objective facts and uncertain subjective information, such as a mood. For example, if an action leads to good results consecutively, people performing the action overestimate the behavioral result of the next action. In electroencephalogram measurements, negative feedback evokes negative potential, called feedback negativity, and positive feedback evokes positive potential, called reward positivity. The present study investigated the relationship between the degree of the mood caused by the feedback outcome and the error-related brain potentials. We measured the electroencephalogram activity while the participants played a virtual reality shooting game. The experimental task was to shoot down a cannonball flying toward the player using a handgun. The task difficulty was determined from the size and curve of the flying cannonball. These gaming parameters affected the outcome probability of shooting the target in the game. We also implemented configurations in the game, such as the player’s life points and play times. These configurations affected the outcome magnitude of shooting the target in the game. Moreover, we used the temporal accuracy of shooting in the game as the parameter of the mood. We investigated the relationship between these experimental features and the event-related potentials using the single-trial-based linear mixed-effects model analysis. The feedback negativity was observed at an error trial, and its amplitude was modulated with the outcome probability and the mood. Conversely, reward positivity was observed at hit trials, but its amplitude was modulated with the outcome probability and outcome magnitude. This result suggests that feedback negativity is enhanced according to not only the feedback probability but also the mood that was changed depending on the temporal gaming outcome.

The Global Navigation Satellite System-Acoustic ranging combination technique (GNSS-A) has enabled us to measure seafloor crustal deformation in the precision of centimeters, leading to numerous discoveries of subseafloor tectonic phenomena. The moving observation conducted by our research group allows us to measure both the horizontal and vertical absolute positions of a reference point on the seafloor. However, the observation frequency of our GNSS-A observation system is still insufficient to observe short-term phenomena. This paper focused on the possibility to reduce the observation time per a seafloor site by shrinking the seafloor transponder array size and the survey line radius, which were empirically defined to be equal to the seafloor site depth in the early research. We evaluated the effects of changing these sizes on the GNSS-A positioning accuracy by conducting a series of numerical experiments. The results of the numerical experiments indicated that for a seafloor site with a depth of 3,000 m, the positioning accuracy is rapidly degraded as the transponder array size and the survey line radius are reduced to less than 3,000 m. Additional experiments done for transponder array sizes and survey line radii around 2,000–4,000 m revealed that shrinking the survey line radius has a dominant effect on the decrease in positioning accuracy. Thus, shrinking the transponder array size and the survey line radius is not a suitable option for reducing observation time, and the empirically defined observation configurations are concluded to be quite optimal when regarding both the positioning accuracy and the observation time.

Global Navigation Satellite System–Acoustic ranging combined seafloor geodetic technique (GNSS-A) has extended the geodetic observation network into the ocean. The key issue for analyzing the GNSS-A data is how to correct the effect of sound speed variation in the seawater. We constructed a generalized observation equation and developed a method to directly extract the gradient sound speed structure by introducing appropriate statistical properties in the observation equation, especially the data correlation term. In the proposed scheme, we calculate the posterior probability based on the empirical Bayes approach using the Akaike’s Bayesian Information Criterion for model selection. This approach enabled us to suppress the overfitting of sound speed variables and thus to extract simpler sound speed field and stable seafloor positions from the GNSS-A dataset. The proposed procedure is implemented in the Python-based software “GARPOS” (GNSS-Acoustic Ranging combined POsitioning Solver).

The Global Navigation Satellite System-Acoustic ranging combination technique (GNSS-A) is a recently developed technology to precisely detect seafloor crustal deformation. This method can also estimate km-scale underwater sound speed structure (SSS) as a by-product of monitoring seafloor crustal deformation. This paper evaluates the validity of the spatial gradient and its temporal variation of the SSS estimated by GNSS-A observations off the Kii channel before and after Kuroshio meandering. According to the comparison of the JCOPE2M reanalysis data and the in situ observation data, the GNSS-A estimated SSS has local structures that are not reproduced in this reanalysis but were detected by in situ data. In addition, we investigate the effect of observation time on the stability of SSS estimation. The results suggest that the sufficient time required for stable estimation depends on the spatial coverage of observation data, which depends on the depth of the site. As a result, the time resolution was derived to be about one hour at a site whose depth is 1500 m.

The Global Navigation Satellite System-Acoustic ranging combination technique (GNSS-A) is a seafloor geodetic technique that enables precise global seafloor positioning to detect subseafloor geophysical phenomena. The technique requires a sea surface observation platform that combines GNSS positioning and acoustic ranging. Currently, a survey vessel is used as the platform, which entails substantial financial and human resources costs, which makes increasing observation frequency difficult. It is possible to detect long-term average seafloor movement at the centimeter level, but it is difficult to detect short-term variation due to the insufficiency of observation frequency. The terrestrial GNSS observation network has detected temporal changes in crustal deformation fields. These precise observations provide useful information on the megathrust seismogenic zone. To detect such phenomena on the seafloor, the temporal resolution of the GNSS-A observation needs to be improved. Advances in vessel equipment technology are crucial for increasing observation frequency. In this paper, we review the historical development of the Japan Coast Guard’s GNSS-A observation system, focusing on technological developments of on-board equipment installed on a sea surface platform, and explain how such improvements have increased observation frequency over time. In the present, ranging frequency has improved from 40–60 s to 15–20 s by the introduction of the multiple ranging system, resulting in more frequent observations up to five times per year for an individual site.

The purpose of this study is to clarify whether there is a learning effect on brain activity after writing with an ink pen vs. a digital pen. Previous studies have reported the superiority of handwriting to typing in terms of learning performance, but differences between the use of an ink pen vs. a digital pen remain unclear. In the present study, the participants learned to read difficult words by writing with an ink pen vs. a digital pen. After the learning period, electroencephalography (EEG) signals were measured, while the participants underwent a repetition priming paradigm with the use of the learned words. The repetition priming effect of the N400 event-related potential (ERP) was quantified as an index of the learning effect and the effects between pen types were compared. The groups were also subdivided according to whether a digital pen is frequently used (familiar vs. unfamiliar group). The number of writing repetitions for each word within 10 min during the learning activity and the post-learning test scores were not affected by the pen-type or familiarity with a digital pen. However, the repetition priming effect of the N400 was greater for words written with a digital pen in the learning session, as compared with an ink pen, in the familiar group, but not the unfamiliar group. These results suggest that for those familiar with its use, writing with a digital pen may improve learning relative to the use of an ink pen.

Because of relative simplicity of signal transduction pathway, bacterial chemotaxis sensory systems have been expected to be applied to biosensor. Tar and Tsr receptors mediate chemotaxis of Escherichia coli and have been studied extensively as models of chemoreception by bacterial two-transmembrane receptors. Such studies are typically conducted using two canonical ligands: l-aspartate for Tar and l-serine for Tsr. However, Tar and Tsr also recognize various analogs of aspartate and serine; it remains unknown whether the mechanism by which the canonical ligands are recognized is also common to the analogs. Moreover, in terms of engineering, it is important to know a single species of receptor can recognize various ligands to utilize bacterial receptor as the sensor for wide range of substances. To answer these questions, we tried to extract the features that are common to the recognition of the different analogs by constructing classification models based on machine-learning. We computed 20 physicochemical parameters for each of 38 well-known attractants that act as chemoreception ligands, and 15 known non-attractants. The classification models were generated by utilizing one or more of the seven physicochemical properties as descriptors. From the classification models, we identified the most effective physicochemical parameter for classification: the minimum electron potential. This descriptor that occurred repeatedly in classification models with the highest accuracies, This descriptor used alone could accurately classify 42/53 of compounds. Among the 11 misclassified compounds, eight contained two carboxyl groups, which is analogous to the structure of characteristic of aspartate analog. When considered separately, 16 of the 17 aspartate analogs could be classified accurately based on the distance between their two carboxyl groups. As shown in these results, we succeed to predict the ligands for bacterial chemoreceptors using only a few descriptors; single descriptor for single receptor. This result might be due to the relatively simple topology of bacterial two-transmembrane receptors compared to the G-protein-coupled receptors of seven-transmembrane receptors. Moreover, this distance between carboxyl groups correlated with the receptor binding affinity of the aspartate analogs. In view of this correlation, we propose a common mechanism underlying ligand recognition by Tar of compounds with two carboxyl groups.

Workload in the human brain can be a useful marker of internal brain state. However, due to technical limitations, previous workload studies have been unable to record brain activity via conventional electroencephalography (EEG) and magnetoencephalography (MEG) devices in mobile participants. In this study, we used a wearable EEG system to estimate workload while participants walked in a naturalistic environment. Specifically, we used the auditory steady-state response (ASSR) which is an oscillatory brain activity evoked by repetitive auditory stimuli, as an estimation index of workload. Participants performed three types of N-back tasks, which were expected to command different workloads, while walking at a constant speed. We used a binaural 500 Hz pure tone with amplitude modulation at 40 Hz to evoke the ASSR. We found that the phase-locking index (PLI) of ASSR activity was significantly correlated with the degree of task difficulty, even for EEG data from few electrodes. Thus, ASSR appears to be an effective indicator of workload during walking in an ecologically valid environment.