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Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0–200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods—single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude—to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0–200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.

The P300 event-related potential, evoked by attending to specific sensory stimuli, is utilized in non-invasive brain–computer interface (BCI) systems and is considered the only interface through which individuals with complete paralysis can operate devices based on their intention. Conventionally, visual stimuli used to elicit P300 have been presented using displays; however, placing a display directly in front of the user obstructs the field of view and prevents the user from perceiving their surrounding environment. Moreover, every time the user changes posture, the display must be repositioned accordingly, increasing the burden on caregivers. To address these issues, we propose a novel system that employs wirelessly controllable LED visual stimulus presentation devices distributed throughout the surrounding environment, rather than relying on traditional displays. The primary challenge in the proposed system is the communication delay associated with wireless control, which introduces errors in the timing of stimulus presentation—an essential factor for accurate P300 analysis. Therefore, it is necessary to evaluate how such delays affect P300 detection accuracy. The second challenge lies in the variability of visual stimulus strength due to differences in viewing distance caused by the spatial distribution of stimulus devices. This also requires the validation of its impact on P300 detection. In Experiment 1, we evaluated system performance in terms of wireless communication delay and confirmed an average delay of 352.1 ± 30.9 ms. In Experiment 2, we conducted P300 elicitation experiments using the wireless visual stimulus presentation device under conditions that allowed the precise measurement of stimulus presentation timing. We compared P300 waveforms across three conditions: (1) using the exact measured stimulus timing, (2) using the stimulus timing with a fixed compensation of 350 ms for the wireless delay, and (3) using the stimulus timing with both the 350 ms fixed delay compensation and an additional pseudo-random error value generated based on a normal distribution. The results demonstrated the effectiveness of the proposed delay compensation method in preserving P300 waveform integrity. In Experiment 3, a system performance verification test was conducted on 21 participants using a wireless visual presentation device. As a result, statistically significant differences (p < 0.01) in amplitude between target and non-target stimuli, along with medium or greater effect sizes (Cohen’s d: 0.49–0.61), were observed under all conditions with an averaging count of 10 or more. Notably, the P300 detection accuracy reached 85% with 40 averaging trials and 100% with 100 trials. These findings demonstrate that the system can function as a P300 speller and be utilized as an interface equivalent to conventional display-based methods.

The yips, an involuntary movement impediment that affects performance in skilled athletes, is commonly described as a form of task-specific focal dystonia or as a disorder lying on a continuum with focal dystonia at one end (neurological) and chocking under pressure at the other (psychological). However, its etiology has been remained to be elucidated. In order to understand sensorimotor cortical activity associated with this movement disorder, we examined electroencephalographic oscillations over the bilateral sensorimotor areas during a precision force task in athletes with yips, and compared them with age-, sex-, and years of experience-matched controls. Alpha-band event-related desynchronization (ERD), that occurs during movement execution, was greater in athlete with yips as compared to controls when increasing force output to match a target but not when adjusting the force at around the target. Event-related synchronization that occurs after movement termination was also greater in athletes with yips. There was no significant difference in task performance between groups. The enhanced ERD is suggested to be attributed to dysfunction of inhibitory system or increased allocation of attention to the body part used during the task. Our findings indicate that sensorimotor cortical oscillatory response is increased during movement initiation in athletes with yips.

The FinRay soft gripper achieves passive enveloping grasping through its functional flexible structure, adapting to the contact configuration of the object to be grasped. However, variations in beam position and thickness lead to different behaviors, making it important to research the relationship between structure and force. Conventional research using FEM simulations has tested various virtual FinRay models but replicating phenomena such as buckling and slipping has been challenging. While hardware-based methods that involve installing sensors on the gripper and the object to analyze their states have been attempted, no studies have focused on the tangential contact force related to slipping. Therefore, we developed a 16-way object contact force measurement device incorporating two-axis force sensors into each of the 16 segmented objects and compared the normal and tangential components of the enveloping grasping force of the FinRay soft gripper under two types of contact friction conditions. In the first experiment, the proposed device was compared with a device containing a six-axis force sensor in one segmented object, confirming that the proposed device has no issues with measurement performance. In the second experiment, comparisons of the proposed device were made under various conditions: two contact friction states, three object contact positions, and two object motion states. The results demonstrated that the proposed device could decompose and analyze the grasping force into its normal and tangential components for each segmented object. Moreover, low friction conditions result in a wide contact area with lower tangential frictional force and a uniform normal pushing force, achieving effective enveloping grasping.

The Fin Ray-type soft gripper (FRSG) is a typical soft gripper structure and applies the deformation characteristics of the Fin Ray structure. This structure functions to stabilize the grasping of an object by passive deformation due to external forces. To analyze the performance of detailed force without compromising the actual FRSG characteristics, it is effective to incorporate multiple force sensors into the grasping object without installing them inside the Fin Ray structure. Since the grasping characteristics of the FRSG are greatly affected by the arrangement of the crossbeams, it is also important to understand the correspondence between the forces and the geometry. In addition, the grasping characteristics of an angular object have not been verified in actual equipment. Therefore, in this study, a contact force measurement device with 16 force sensors built into the grasping object and a structural deformation measurement device using camera images were used to analyze the correspondence between force and structural deformation on an actual FRSG. In the experiment, we analyzed the influence of the crossbeam arrangement on the grasping force and the grasping conditions of the square (0°) and rectangular (45°) shapes, and state that an ideal grasp in a square-shaped (45°) grasp is possible if each crossbeam in the FRSG is arranged at a different angle.

It has been suggested that resting-state functional connectivity (rs-FC) between the primary motor area (M1) region of the brain and other brain regions may be a predictor of motor learning, although this suggestion is still controversial. In the work reported here, we investigated the relationship between M1 seed-based rs-FC and motor learning. Fifty-three healthy volunteers undertook random button-press and sequential motor learning tasks. Five-minute resting-state data acquisition was performed between the two tasks. Oscillatory neural activities during the random task and the rest period were measured using magnetoencephalography. M1 seed-based rs-FC was calculated for the alpha and beta bands using amplitude envelope correlation, in which the seed location was defined as an M1 position with peak event-related desynchronization value. The relationship between rs-FC and the performance of motor learning was examined using whole brain correlation analysis. The results showed that beta-band resting-state cross-network connectivity between the sensorimotor network and the core network, particularly the theory of mind network, affected the performance of subsequent motor learning tasks. Good learners could be distinguished from poor learners by the strength of rs-FC between the M1 and the left superior temporal gyrus, a part of the theory of mind network. These results suggest that cross-network connectivity between the sensorimotor network and the theory of mind network can be used as a predictor of motor learning performance.

Transcutaneous vagus nerve stimulation (tVNS) has emerged as a promising non-invasive technique for modulating neuroplasticity. Previous studies have suggested that changes in regional brain GABA+ signaling contribute to these effects, but empirical neurophysiological evidence remains limited. We investigated the neurophysiological and behavioral effects of tVNS (200-μs pulses at 20 Hz, alternating 30 s ON-1 s OFF cycles, 30 min total duration) in healthy adults using two experimental paradigms. In Experiment 1, GABA+ and Glutamate (Glu) levels were measured in the left striatum (STR), dorsolateral prefrontal cortex (DLPFC), and sensorimotor cortex (SM) of 34 participants by proton magnetic resonance spectroscopy ( H-MRS), before and after ipsilateral tVNS. In Experiment 2, 27 participants performed a right-hand force-control motor learning task before, during, and after tVNS. Administration of tVNS significantly reduced GABA+ levels in the left STR (  < 0.05), increased Glu levels in the DLPFC ( < 0.05), and significantly improved motor task performance compared to the sham group at 10 min after stimulus onset (  < 0.05). tVNS (1) reduced striatal GABA+ levels and increased DLPFC Glu in healthy adults, and (2) facilitated early-phase motor learning. These findings support the use of tVNS as a noninvasive intervention that enhances motor learning in neurorehabilitation and the treatment of motor disorders.

Models trained with Deep Reinforcement learning (DRL) have been deployed in various areas of robotics with varying degree of success. To overcome the limitations of data gathering in the real world, DRL training utilizes simulated environments and transfers the learned policy to real-world scenarios, i.e., sim–real transfer. Simulators fail to accurately capture the entire dynamics of the real world, so simulation-trained policies often fail when applied to reality, termed a reality gap (RG). In this paper, we propose a search (mapping) algorithm that takes in real-world observation (images) and maps them to the policy-equivalent images in the simulated environment using a convolution neural network (CNN) model. The two-step training process, DRL policy and a mapping model, overcomes the RG problem with simulated data only. We evaluated the proposed system with a gripping task of a custom-made robot arm in the real world and compared the performance against a conventional DRL sim–real transfer system. The conventional system achieved a 15–57% success rate in gripping operation depending on the position of the target object while the mapping-based sim–real system achieved 100%. The experimental results demonstrated that the proposed DRL with mapping method appropriately corresponded the real world to the simulated environment, confirming that the scheme can achieve high sim–real generalization at significantly low training costs.

This paper sought to improve the precision of the Alternating Current Electro-Occulo-Graphy (AC-EOG) gaze estimation method. The method consisted of two core techniques: To estimate eyeball movement from EOG signals and to convert signals from the eyeball movement to the gaze position. In conventional research, the estimations are computed with two EOG signals corresponding to vertical and horizontal movements. The conversion is based on the affine transformation and those parameters are computed with 24-point gazing data at the calibration. However, the transformation is not applied to all the 24-point gazing data, but to four spatially separated data (Quadrant method), and each result has different characteristics. Thus, we proposed the conversion method for 24-point gazing data at the same time: To assume an imaginary center (i.e., 25th point) on gaze coordinates with 24-point gazing data and apply an affine transformation to 24-point gazing data. Then, we conducted a comparative investigation between the conventional method and the proposed method. From the results, the average eye angle error for the cross-shaped electrode attachment is x = 2.27 ° ± 0.46 ° and y = 1.83 ° ± 0.34 ° . In contrast, for the plus-shaped electrode attachment, the average eye angle error is is x = 0.94 ° ± 0.19 ° and y = 1.48 ° ± 0.27 ° . We concluded that the proposed method offers a simpler and more precise EOG gaze estimation than the conventional method.

Metabolic disorders can induce psychiatric comorbidities. Both brain and neuronal composition imbalances reportedly induce an anxiety-like phenotype. We hypothesized that alterations of localized brain areas and cholecystokinin (CCK) and parvalbumin (PV) expression could induce anxiety-like behavior in type 2 diabetic Otsuka Long-Evans Tokushima fatty (OLETF) rats. Twenty-week-old OLETF and non-diabetic Long-Evans Tokushima Otsuka (LETO) rats were used. The areas of corticolimbic regions were smaller in OLETF rats. The densities of CCK positive neurons in the lateral and basolateral amygdala, hippocampal cornu ammonis area 2, and prelimbic cortex were higher in OLETF rats. The densities of PV positive neurons were comparable between OLETF and LETO rats. Locomotion in the center zone in the open field test was lower in OLETF rats. These results suggest that imbalances of specific brain region areas and neuronal compositions in emotion-related areas increase the prevalence of anxiety-like behaviors in OLETF rats.