Biology subjects

Ushiba, J.

Publications and source records attributed to Ushiba, J..

11 recordsLinked to original sources

bioRxiv2026

Design and validation of a novel portable electrode holder for motor-related EEG measurement

ObjectiveA simplified headset to measure electroencephalography (EEG) from sensorimotor areas is necessary to monitor motor-related neural responses accurately in real-world environments. The aim of the present study was to design and validate a novel, easy-to-use, and reliable EEG electrode holder that enables positioning of the EEG electrodes directly over the sensorimotor cortex, while enabling flexibility in relation to varying head size. ApproachThe spatial distribution of motor-related EEG activity was estimated using a dataset of high-density EEG (HD-EEG) from 82 participants. International databases of head shape were analyzed to determine dimensional requirements for a headphone-shaped holder. The proposed headset was validated by comparing its recordings with those obtained with a commercially available HD-EEG (Experiment 1) and by computing the actual position and recorded EEG motor-related activity obtained using the proposed holder (Experiment 2). Main resultsThe estimated distance between the measurement electrode and the top of the head, used as a design requirement for the proposed headset, ranged from 56.2 to 80.0 mm. Experiment 1 showed that the center frequencies of alpha-band recorded by the proposed and by the HD-EEG headsets were highly correlated (r=0.97). Experiment 2 showed that, by using the proposed headset, the actual placement of electrodes was within 8 mm from the ideal positions. Moreover, the experiment showed consistent results in terms of task-, location-, and frequency-specific modulation of sensorimotor activities in the alpha-band. For example, significant contralateral motor-related event-related desynchronization in the alpha-band, and significant alpha-band power increase during the eyes closed condition, namely event-related synchronization. SignificanceThe proposed electrode holder is easy to use and adjustable to compensate for varying head size and it may enable reliable measurement of motor-related EEG. It could support practical application of motor-related EEG acquisition in real-world contexts in several applications including sports, rehabilitation, and artistic performance.

neuroscience↗

bioRxiv2025

Pre-movement neural population activity in human motor cortex reflects the subsequent outcome of futsal free kicks

Brain states immediately before movement may shape skilled athletic performance, but translating electroencephalography (EEG)-based monitoring to sport has been limited by dense montages and laboratory-constrained tasks. We asked whether a portable three-channel EEG system can detect outcome-related preparatory activity during a realistic futsal free-kick task. EEG from C3, Cz, and C4 during the 2 s before each kick was used to decode successful versus unsuccessful trials with channel-wise linear support vector machines (SVMs), and the same approach was tested for transfer across recording days. To relate decoding to interpretable physiology, alpha- and beta-band power were also compared between outcomes. Successful and failed kicks were decoded significantly above label-shuffled null distributions within recording days across all channels, showing that low-density EEG can capture sensor-level information linked to subsequent performance. However, decoding did not generalize reliably across days, indicating that these neural signatures were session specific. Spectral analysis showed selectively increased C3 beta-band power before successful kicks, whereas alpha-band power showed no significant outcome-related differences. These findings support portable EEG as a practical route toward monitoring preparatory brain states in sport, while suggesting that real-world use will require short day-specific calibration or adaptive decoding.

bioengineering↗

bioRxiv2025

Chronotype-specific impacts of sleep-wake schedule on behavioral, physiological and psychological parameters

Diurnal variations in human performance are known to be associated with chronotype, which reflects individual differences in preferred sleep-wake cycles. Early chronotypes (ECs) exhibit superior motor and cognitive functions in the morning, whereas late chronotypes (LCs) tend to perform better in the evening. However, previous studies have examined these patterns under socially imposed nighttime sleep schedules, without considering individuals preferred sleep timing. Thus, it remains unclear whether these chronotype-dependent performance peaks persist when individuals follow their preferred sleep schedules. To address this question, we measured subjective states, sleep pressure (using electroencephalography), and motor and cognitive functions at 1.5 and 12.5 hours after awakening under two conditions: the Clock-Time condition (10:30 PM-6:30 AM sleep aligned with social rhythms) and the Self-Time condition (8 hours of sleep aligned with individual preferences). The results revealed distinct chronotype-specific patterns. ECs exhibited increases in fatigue and sleepiness dependent on awake duration, showing typical sleep pressure accumulation. LCs were more affected by sleep timing and showed atypical patterns of sleep pressure accumulation compared with ECs. Motor ability tended to improve in the afternoon in both chronotypes, irrespective of sleep-wake schedule, whereas cognitive performance peaked at different stages of wakefulness - earlier for ECs and later for LCs - independent of the time of day. These findings suggest that chronotype-aligned sleep-wake schedules could help optimize fatigue management, cognitive performance, and motor control. Diurnal variations in performance cannot be attributed to clock time alone but emerge from the combined effects of chronotype, awake duration, and sleep-wake regulation. Statement of SignificanceThis study provides new insights into the ways in which chronotype and sleep-wake schedules jointly shape behavioral, physiological, and psychological dynamics across the day. Whereas previous research has largely emphasized circadian rhythms, our findings underscore the critical role of actual sleep timing in modulating daily function ing. This integrative perspective extends current understanding of the ways in which individual differences in biological timing and social constraints interact to influence human health and performance. Importantly, the results highlight the need for future studies to disentangle circadian and sleep-wake schedule effects, which could inform personalized strategies for optimizing well-being, productivity, and clinical interventions in disorders related to sleep and circadian misalignment.

neuroscience↗

bioRxiv2025

Neural correlates of individual variability in reward-based motor learning

Individual differences in motor learning are linked to variability in the neural processes associated with feedback processing and motor memory retention. This study investigated the neural correlates of reward-based motor learning and focused on two electrophysiological markers: event-related desynchronization (ERD) in the sensorimotor cortex and feedback-related negativity (FRN) from frontal EEG channels. Sixty-four healthy adults performed a visuomotor rotation task with monetary reward or punishment feedback. Learning amount, defined as the degree of error compensation, and retention amount, defined as the maintenance of adapted motor behavior in a no-vision condition, were quantified. Scalp EEG was recorded to assess alpha (8-13 Hz) and beta (15-30 Hz) ERD over the primary motor cortex (M1) during movement preparation and FRN amplitude following feedback presentation. Stepwise regression revealed that FRN amplitude in the late adaptation phase was associated with learning amount, but only among participants with robust event-related potential (ERP) responses (amplitude > {+/-}5 {micro}V). This finding suggests that greater neural sensitivity to feedback supports better learning. In contrast, alpha ERD magnitude in the late adaptation phase, along with reinforcement conditions, was a significant predictor of motor skill retention in the full sample, although no interaction effect was observed. These findings suggest that FRN and alpha ERD reflect dissociable neural mechanisms underlying learning and retention, respectively, with FRN indexing feedback sensitivity and alpha ERD reflecting cortical excitability related to memory consolidation. The absence of interaction effects between neural markers and reinforcement conditions further supports the idea that these processes contribute independently to motor adaptation. Practically, combining reward-based training with interventions that increase M1 excitability may enhance retention, while tailoring feedback strategies to maximize FRN responses may improve learning efficiency. These findings provide novel insights into the neural basis of individual variability in motor learning and suggest avenues for personalized approaches in rehabilitation and skill learning.

neuroscience↗

bioRxiv2025

Human intention inference with a large language model can enhance brain-computer interface control: A proof-of-concept study

Brain-computer interface (BCI) control enables direct communication between the brain and external devices. However, the accuracy of BCI control based on intentions inferred from unstable neural signals remains limited, even with data-driven approaches to tailor neural decoders. Here we propose a knowledge-driven framework for inferring human intentions, leveraging large language models (LLMs) that incorporate prior knowledge about human behavior and neural activity. We developed a neural decoder that integrates neural and oculomotor signals with contextual information using an LLM agent. Its feasibility was tested in a real-world BCI task involving interaction with a computer application. The LLM-based decoder achieved an average accuracy of 79% among participants whose neural signals were responsive (11 out of 20) in inferring the intention to select arbitrary posts in a social networking service. Ablation analyses revealed that the integration of contextual information, multimodal signals, and empirical knowledge is critical for decoding accuracy. This study demonstrates the feasibility of a neural decoding framework using an LLM, paving the way for improved performance in BCI-driven external device operation for individuals with disabilities. HighlightsO_LILarge language models can infer human intent by integrating neural and oculomotor signals with screen context. C_LIO_LIThe proposed model outperforms conventional data-driven approaches in decoding accuracy. C_LIO_LIAblation analyses reveal that integrating contextual information, multimodal signals, and empirical knowledge is critical for accurate decoding. C_LI

neuroscience↗

bioRxiv2024

Enhanced human sensorimotor integration via self-modulation of the somatosensory activity

Kinesthetic motor imagery, the mental rehearsal of movement and its associated sensations1,2, activates somatosensory and motor cortices3,4. Motor imagery improves performance in neurorehabilitation5, sports6, and instrument play7. However, the mechanism that enhances performance through repetitive motor imagery remains unclear. Here, we combined individually tailored motor imagery training combined with closed-loop neurofeedback training, neurophysiology, and behavioral assessment to characterize how the training can modulate the somatosensory system and improve performance. The closed-loop training using real-time feedback of human electroencephalogram (EEG) signals enhanced participants self-modulation ability of intrinsic neural oscillations in the primary somatosensory cortex (S1) within 30 minutes. Further, the short-term reorganization in S1 was corroborated by the post-training reactivity enhancement revealed by changes in somatosensory evoked potential (SEP) amplitude of early component originating from S18. Meanwhile those derived from peripheral sensory fibers were maintained, suggesting that the closed-loop training manipulated cortical activities. Behavioral evaluation demonstrated improved performance during keyboard touch-typing indexed by resolved the speed-accuracy trade-off9. Collectively, our results provide evidence that mentally rehearsed movement during kinesthetic motor imagery induces functional reorganization of S1 activities that can contribute to performance improvement.

neuroscience↗

bioRxiv2024

Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation

In this study, rapid topographical changes were detected in the forelimb motor maps in the primary motor cortex (M1) of awake marmoset monkeys using our previously developed accurate short-time stimulation mapping procedure (Takemi et al. 2017; Kosugi et al. 2018). The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. The flexible nature of the motor maps necessitates the reconsideration of the results of motor control and neuroplasticity studies. Neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state warrant further exploration.

neuroscience↗

bioRxiv2022

Two common issues in synchronized multimodal recordings with EEG: Jitter and Latency

Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., electromyograms, eye movement, pupil information, or limb kinematics) is ubiquitous in human neuroscience research. However, the precise time alignment of data from heterogeneous sources is limited due to variable recording parameters of commercially available research devices and experimental setups. Here, we introduced the versatility of a Lab Streaming Layer (LSL)-based application for multimodal recordings of high-density EEG and other devices such as eye trackers or hand kinematics. To introduce the benefit of recording multiple devices in a time-synchronized manner, we discuss two common issues in measuring multimodal data: jitter and latency. The LSL-based system can be used for research on precise time-alignment of datasets, such as detecting stimulus-induced transient neural responses and testing hypotheses well-formulated in time by leveraging the millisecond time resolution of the system.

neuroscience↗

bioRxiv2021

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric rebalancing of M1 excitability

Human behavior requires interregional crosstalk to employ the sensorimotor processes in the brain. Although some external neuromodulation tools have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We overcame this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable participants to manipulate their bilateral sensorimotor activities. Herein, we report that bi-directional changes in ipsilateral excitability to the imagined hand (conditioning hemisphere) affect interhemispheric inhibition (IHI) assessed by paired-pulse transcranial magnetic stimulation paradigm. In addition, participants were able to robustly manipulate the IHI magnitudes. Further physiological analyses revealed that the self-manipulation of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results provide clear neuroscientific evidence regarding the inhibitory interhemispheric sensorimotor activity and IHI manipulator, thereby challenging the current theoretical concept of recovery of motor function for neurorehabilitation.

neuroscience↗

bioRxiv2021

De novo brain-computer interfacing deforms manifold of populational neural activity patterns in human cerebral cortex

Human brains are capable of modulating innate activities to adapt to novel environmental stimuli; for sensorimotor cortices (SM1) this means acquisition of a rich repertoire of motor behaviors. We investigated the adaptability of human SM1 motor control by analyzing net neural population activity during the learning of brain-computer interface (BCI) operations. We found systematic interactions between the neural manifold of cortical population activities and BCI classifiers; the neural manifold was stretched by rescaling motor-related features of electroencephalograms with model-based fixed classifiers, but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, operation of a BCI based on a de novo classifier with a fixed decision boundary based on biologically unnatural features, deformed the neural manifold to be orthogonal to the boundary. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/450263v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@12a66c3org.highwire.dtl.DTLVardef@c090e8org.highwire.dtl.DTLVardef@698f87org.highwire.dtl.DTLVardef@4e135c_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2017

Brain-Computer Interfaces for Post-Stroke Motor Rehabilitation: A Meta-Analysis

ObjectiveBrain-computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here we provide a first meta-analysis evaluating the clinical effectiveness of BCI-based post-stroke motor rehabilitation.\n\nMethodsTrials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post-stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random-effects inverse variance method was used to calculate the summary effect size.\n\nResultsWe initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post-stroke survivors fulfilling the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta-analysis. Motor improvements, mostly quantified by the upper limb Fugl-Meyer Assessment (FMA-UE), exceeded the minimal clinical important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference (SMD) of 0.79 (95% CI: 0.37 to 1.20) in FMA-UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI-induced functional and structural neuroplasticity at a sub-clinical level.\n\nInterpretationWe found a medium to large effect size of BCI therapy compared to controls. This suggests that BCI technology might be an effective intervention for post-stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

neuroscience↗