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Background: Stroke patients typically suffer from a range of symptoms, such as motor and language impairments, due to shared neural networks. The recovery process after stroke is intricate and requires a comprehensive approach. While previous studies have investigated the motor and language interventions independently, this study aimed to explore the relationship between these domains and compared the effectiveness of individual interventions versus their combined use. Methods: We divided 45 stroke patients into three groups: Speech and Language Therapy (SLT) group; Arm Ability Training (AAT) group; and consecutive combination of SLT and AAT group. Participants attended 40-min sessions three days a week for three weeks. Standardized assessments, including picture naming test, syntactic comprehension test, and Test d'Evaluation des Membres Supérieurs de Personnes Âgées (TEMPA) test, were conducted pre-and post-treatment and during the first and second weeks of the intervention. Results: Within-group comparisons demonstrated a significant enhancement in test scores for all groups post-intervention compared to pre-intervention (p < 0.05). Between-group comparisons revealed significant differences (p < 0.05) in performance on the picture naming test during the first week, the syntactic comprehension test in the second week, the functional rating subscale of Tempa test changes pre-and post-treatment and the first week, and the length of the time subscale of Tempa test improvements from pre-intervention to the first week. Conclusion: Findings underscored the mutual and synergistic benefits of integrating motor and language in stroke rehabilitation. While SLT and AAT were effective when applied independently, their combined application yielded superior outcomes, emphasizing the holistic advantages of integrating these interventions, as supported by existing literature on dual-task rehabilitation strategies. Clinical trial registration: https://irct.behdasht.gov.ir/search/result?query=IRCT20200114046134N1, IRCT20200114046134N1. [ABSTRACT FROM AUTHOR]

Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis.Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands.Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands.Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.

Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1) and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤13 Hz) and γ frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, d oscillations were greater during contralateral reach and grasp, and ß activity increased during contralateral grasp. In contrast, ß oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces. [ABSTRACT FROM AUTHOR]

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.

Learning to write involves integrating motor production and visual perception to develop orthographic representations. This study tries to test the effect of hand movement training as a pathway to neural correlates for L2 Chinese and L2 English readers. Twenty L2 Chinese and 20 L2 English (n = 20) adults participated in both behavioral and electroencephalogram (EEG) experiments. We designed six learning conditions: Hand Writing Chinese (HC), Viewing Chinese (VC), Drawing followed by Character Recognition in Chinese (DC), Hand Writing English (HE), Viewing English (VE), and Drawing followed by Word Recognition in English (DE). Behavioral and EEG results demonstrated that drawing facilitated visual word recognition in Chinese compared to viewing. The findings imply that hand movement could strengthen the neural processing and improve behavioral performance in Chinese character recognition for L2 Chinese learners and English word recognition for L2 Chinese learners. Furthermore, N170 amplitude at the drawing condition was positively correlated with N400 amplitudes. Thus, the early visual word recognition neural indicator (e.g., N170) was predictive of the late neural indicator of semantic processing (e.g., N400), suggesting that hand movement facilitates the neural correlates between early word recognition and later comprehension. [ABSTRACT FROM AUTHOR]

Office workers often resort to therapeutic systems to address muscular and nerve disorders caused by poor gestures and extraordinary workloads. These systems deliver effective treatment with a playable experience. However, the therapeutic evaluation still heavily relies on occupational therapists' diagnose, which is time-consuming and subjective. This highlights the necessity for a system that adheres to standard treatment protocols and enables individualised therapeutic evaluations. In this study, we proposed a hand therapy system based on around-device hand movements with a capacitive-sensing mobile phone case to realise instant yet approximate hand therapy evaluation. Moreover, we conducted empirical studies to investigate the usability and acceptability of this system and its potential influence on office workers' willingness to take hand therapy exercises during working hours. The results showed that this instant yet approximate evaluation system can significantly improve the workers' hand therapy frequencies during working intervals. Furthermore, it can quantitatively measure and report on individual therapy performances, helping office workers understand their therapy outcomes and promoting their willingness to take therapeutic hand exercises. Our results introduce a new perspective for designing mobile systems for well-being. [ABSTRACT FROM AUTHOR]

People with central nervous system disorders typically have difficulties in coordination of the upper limb and hand movements, which significantly impairs their activities of daily living. Laboratory-based measures can provide quantitative and objective information about intra-limb coordination to aid the rehabilitation process of this population. However, there is currently no comprehensive review of laboratory-based measures. The aim of this review was to identify and summarize laboratory-based intra-limb coordination measures for different upper limb and hand movements. Searches were performed in the CINAHL, Embase, IEEE Xplore, MEDLINE, PubMed and Web of Science databases to identify studies published between 2013 and 2022. Two authors independently performed paper selection, data extraction and quality assessment. 21 papers were identified, and six types of coordination measures were classified. These included principal component analysis, continuous relative phase analysis, correlation analysis, regression analysis, uncontrolled manifold analysis, and uncorrelated surrogate data analysis, in descending order of occurrence. Regarding psychometric properties, all measures demonstrated good discriminative validity. However, only the principal component analysis approach and the continuous relative phase analysis approach were found to have good convergent validity and responsiveness, respectively. In terms of their practicality, these measures were primarily utilized for quantifying coordination in individuals with neurological disorders, with a greater emphasis on the coordination of upper limb movements rather than hand movements. This review summarized and critiqued the characteristics of six types of joint coordination measures. Researchers and clinicians should therefore select appropriate measures based on individual needs. Future research should continue on analysing coordination in individuals with pathological conditions and exploring the application of these measures in quantifying hand movement coordination, to advance current knowledge and inform rehabilitation practices. • Six intra-limb joint coordination measures are identified in this review. • Principal component analysis is the most popular measure in the past decade. • Joint coordination is often quantified in patients with neurological disorders. • The quantification of hand movement coordination is relatively inadequate. • Validity and responsiveness of the coordination measures are mostly examined. [ABSTRACT FROM AUTHOR]

Background: Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. Objective: We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. Methods: The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. Results: The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F 1-score (93.3%). Conclusions: The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers. [ABSTRACT FROM AUTHOR]

The present study aimed at examining the effect of mental calculation and number comparison on motor performance measured as the movement time of a fast manual-pointing movement. Three experiments, involving a total number of 65 undergraduate subjects, examined the effect of mental subtraction (complex) and, respectively, of (a) mental addition (simple or complex), (b) mental multiplication (simple or complex), and (c) the comparison of dot sets and number comparison. Each number was written in Arabic. The movement times were analyzed by using a multilevel linear mixed-effect model. The results showed significant improvement of manual-pointing movement performance only after the complex calculations and after number comparison. Possible implication of attentional mechanisms specific to this arithmetical activity is further discussed. [ABSTRACT FROM AUTHOR]

There are few wearable sensors suitable for daily monitoring of wrist and finger movements for hand-related healthcare applications. Here, we describe the development and validation of a novel algorithm for magnetically counting hand movements. We implemented the algorithm on a wristband that senses magnetic field changes produced by movement of a magnetic ring worn on the finger (the "Manumeter"). The "HAND" (Hand Activity estimated by Nonlinear Detection) algorithm assigns a "HAND count" by thresholding the real-time change in magnetic field created by wrist and/or finger movement. We optimized thresholds to achieve a HAND count accuracy of ~85% without requiring subject-specific calibration. Then, we validated the algorithm in a dexterity-impaired population by showing that HAND counts strongly correlate with clinical assessments of upper extremity (UE) function after stroke. Finally, we used HAND counts to test a recent hypothesis in stroke rehabilitation that real-world UE hand use increases only for stroke survivors who achieve a threshold level of UE functional capability. For 29 stroke survivors, HAND counts measured at home did not increase until the participants' Box and Blocks Test scores exceeded ~50% normal. These results show that a threshold-based magnetometry approach can non-obtrusively quantify hand movements without calibration and also verify a key concept of real-world hand use after stroke.

There are few wearable sensors suitable for daily monitoring of wrist and finger movements for hand-related healthcare applications. Here, we describe the development and validation of a novel algorithm for magnetically counting hand movements. We implemented the algorithm on a wristband that senses magnetic field changes produced by movement of a magnetic ring worn on the finger (the "Manumeter"). The "HAND" (Hand Activity estimated by Nonlinear Detection) algorithm assigns a "HAND count" by thresholding the real-time change in magnetic field created by wrist and/or finger movement. We optimized thresholds to achieve a HAND count accuracy of ~85% without requiring subject-specific calibration. Then, we validated the algorithm in a dexterity-impaired population by showing that HAND counts strongly correlate with clinical assessments of upper extremity (UE) function after stroke. Finally, we used HAND counts to test a recent hypothesis in stroke rehabilitation that real-world UE hand use increases only for stroke survivors who achieve a threshold level of UE functional capability. For 29 stroke survivors, HAND counts measured at home did not increase until the participants' Box and Blocks Test scores exceeded ~50% normal. These results show that a threshold-based magnetometry approach can non-obtrusively quantify hand movements without calibration and also verify a key concept of real-world hand use after stroke.

Rehabilitation is a therapy process that helps people who have lost their motor abilities partially or completely to restore their lost functions. Accuracy and continuity of the repetitive movements in the therapy process are important for the success of the rehabilitation process. Therefore, in order to ensure the continuity of the rehabilitation process, it is necessary to develop systems where exercises can be continued without going to rehabilitation centers. In this study, a robot hand prototype application that imitates both hand and finger movements has been developed for hand rehabilitation applications. An Inertial Measurement Unit (IMU) sensor is used to detect hand movements and a flexible sensor is used to detect finger movements. The hand movements are used to control the two motors on the robot hand prototype, while the flexibility sensor controls the gripper on the robot, allowing the robot hand prototype to grip. The task completion times were recorded by giving the participants the task of moving a cylindrical object to the target point using the robot hand prototype. [ABSTRACT FROM AUTHOR]

Perceptual coherence in the face of discrepant multisensory signals is achieved via the processes of multisensory integration, recalibration and sometimes motor adaptation. These supposedly operate on different time scales, with integration reducing immediate sensory discrepancies and recalibration and motor adaptation reflecting the cumulative influence of their recent history. Importantly, whether discrepant signals are bound during perception is guided by the brains' inference of whether they originate from a common cause. When combined, these two notions lead to the hypothesis that the time scales on which integration and recalibration (or motor adaptation) operate are associated with different time scales of evidence about a common cause underlying two signals. We tested this prediction in a well‐established visuo‐motor paradigm, in which human participants performed visually guided hand movements. The kinematic correlation between hand and cursor movements indicates their common origin, which allowed us to manipulate the common‐cause evidence by titrating this correlation. Specifically, we dissociated hand and cursor signals during individual movements while preserving their correlation across the series of movement endpoints. Following our hypothesis, this manipulation reduced integration compared with a condition in which visual and proprioceptive signals were perfectly correlated. In contrast, recalibration and motor adaption were not affected by this manipulation. This supports the notion that multisensory integration and recalibration deal with sensory discrepancies on different time scales guided by common‐cause evidence: Integration is prompted by local common‐cause evidence and reduces immediate discrepancies, whereas recalibration and motor adaptation are prompted by global common‐cause evidence and reduce persistent discrepancies. [ABSTRACT FROM AUTHOR]

Wearable grip sensing shows potential for hand rehabilitation, but few studies have studied feasibility early after stroke. Here, we studied a wearable grip sensor integrated with a musical computer game (MusicGlove). Among the stroke patients admitted to a hospital without limiting complications, 13% had adequate hand function for system use. Eleven subjects used MusicGlove at home over three weeks with a goal of nine hours of use. On average they achieved 4.1 ± 3.2 (SD) hours of use and completed 8627 ± 7500 grips, an amount comparable to users in the chronic phase of stroke measured in a previous study. The rank-order usage data were well fit by distributions that arise in machine failure theory. Users operated the game at high success levels, achieving note-hitting success >75% for 84% of the 1061 songs played. They changed game parameters infrequently (31% of songs), but in a way that logically modulated challenge, consistent with the Challenge Point Hypothesis from motor learning. Thus, a therapy based on wearable grip sensing was feasible for home rehabilitation, but only for a fraction of subacute stroke subjects. Subjects made usage decisions consistent with theoretical models of machine failure and motor learning.

During rubber hand illusion (RHI), participants feel that a rubber (fake) hand is their own (i.e., embodiment of the rubber hand) if the unseen real hand and seen rubber hand are stroked synchronously (i.e., visuo-tactile stimuli). The RHI is also evoked if the real and rubber hands are placed in the same position (i.e., visual-proprioceptive congruency), which can be performed using a mirror setting. Using electroencephalography (EEG) and mirror settings, we compared μ rhythm (8–13 Hz) event-related desynchronization (ERD; an index of sensorimotor activation) while watching the movements of embodied or non-embodied rubber hands, which was preceded by an observation of the rubber hand with or without synchronous visuo-tactile stimuli. The illusory ownership of the fake hand was manipulated using visual continuity with (RHI) and without (non-RHI) a fake forearm. Resultantly, an ownership-dependent μ rhythm ERD was found when delivering visuo-tactile stimuli; a greater and more persistent μ rhythm ERD during the rubber hand movement was identified in the RHI in comparison to the non-RHI condition. However, no difference was observed between the two when observing the fake hand alone. These findings suggest the possibility that a self-related multisensory interaction between body representation (top-down processing) and visuo-tactile inputs (bottom-up processing) before a fake hand movement produces ownership-dependent sensorimotor activations during subsequent movement observations. [ABSTRACT FROM AUTHOR]

Background: Manual dexterity is the ability to manipulate objects using the hands and fingers for a specific task. Although manual dexterity is widely investigated in the general and special population at all ages, numerous aspects still remain to be explored in children. The aim of this study was to assess the presence of the training effect of the execution of the Grooved Pegboard test (GPT) and to measure the performance of the GPT in dual-task (DT), i.e., during a motor task and a cognitive task. Methods: In this observational, cross-sectional study manual dexterity was assessed in children aged between 6 and 8. The procedure consisted of two phases: (1) the execution of five consecutive trials of the GPT to evaluate the training effect; (2) the execution of one trial of the GPT associated with a motor task (finger tapping test, GPT-FTT), and one trial of the GPT associated with a cognitive task (counting test, GPT-CT) to evaluate the performance in DT. Results: As for the training effect, a significant difference (p

Long-term motor training can cause functional and structural changes in the human brain. Assessing how the training of specific movements affects specific parts of the neural circuitry is essential to understand better the underlying mechanisms of motor training-induced plasticity in the human brain. We report a single-case neuroimaging study that investigated functional and structural properties in a professional athlete of wheelchair racing. As wheelchair racing requires bilateral synchronization of upper limb movements, we hypothesized that functional and structural properties of interhemispheric interactions in the central motor system might differ between the professional athlete and controls. Functional and diffusion magnetic resonance imaging (fMRI and dMRI) data were obtained from a top Paralympian (P1) in wheelchair racing. With 23 years of wheelchair racing training starting at age eight, she holds an exceptional competitive record. Furthermore, fMRI and dMRI data were collected from three other paraplegic participants (P2-P4) with long-term wheelchair sports training other than wheelchair racing and 37 able-bodied control volunteers. Based on the fMRI data analyses, P1 showed activation in the bilateral precentral hand sections and greater functional connectivity between these sections during a right-hand unimanual task. In contrast, other paraplegic participants and controls showed activation in the contralateral hemisphere and deactivation in the ipsilateral hemisphere. Moreover, dMRI data analysis revealed that P1 exhibited significantly lower mean diffusivity along the transcallosal pathway connecting the bilateral precentral motor regions than control participants, which was not observed in the other paraplegic participants. These results suggest that long-term training with bilaterally synchronized upper-limb movements may promote bilateral recruitment of the precentral hand sections. Such recruitment may affect the structural circuitry involved in the interhemispheric interaction between the bilateral precentral regions. This study provides valuable evidence of the extreme adaptability of the human brain. [ABSTRACT FROM AUTHOR]

Objective: Fidgeting is a common symptom in patients with attention-deficit hyperactivity disorder (ADHD). The current study investigated ADHD stimulant medication effects on fidgeting in adolescents with ADHD during a short research study session using wrist-worn accelerometers. Method: Adolescents with ADHD who had been taking stimulant medications (ADHD group) and adolescents without ADHD (control group) participated in the study. Accelerometer data were obtained from both wrists of each participant to track their hand movements during two hearing testing sessions. All subjects in the ADHD group abstained from their stimulant medications for at least 24 hours before their first session (off-med session). The second session (on-med session) was conducted about 60–90 minutes after medication intake. The control group participated in two sessions in a similar time frame. Results: The current study focuses on relationships between hand movements and stimulant medication in adolescents with ADHD. Both conditions were compared to evaluate the relationship of hand movements and stimulant medication. We hypothesized the ADHD group will exhibit less hand movements during the on-medication session in comparison to off-medication session. Conclusion: Wrist-worn accelerometer measures obtained during nonphysical tasks in a short duration may not provide hand movement differences between on-med and off-med conditions in adolescents with ADHD. ClinicalTrials.gov Identifier: NCT04577417. [ABSTRACT FROM AUTHOR]

• There are significant differences in hand dimensions during dynamic situations, with notable increases or decreases in hand length dimensions. • Hand dimensions exhibited significant differences across most dynamic states. • Imaging methods are a suitable alternative to manual anthropometric methods. Effective glove design requires understanding hand dimensions and movement. Designers must study hands in static and dynamic postures. They do this to identify key movement points. This study aimed to determine hand size during motion. The goal was to understand and specify how hand shape changes. Thirty dynamic hand dimensions of 100 Iranian males were measured in six postures using photography and three-dimensional modeling. The data were classified and compared based on hand positions. The results showed significant differences in hand size during movements. Four hand. dimensions were found to have significant differences (p > 0.05) related to hand posture. Sizes either decreased or increased significantly. The dorsal hand length underwent a significant alteration. This was compared to the palmar hand length during movement. The results of this study help to design gloves with optimal fit, performance and comfort. The results show that the dorsal hand length should be increased, while the palmar hand length should be decreased in the design of the glove pattern. Gloves that are currently made based on static postures cannot provide a good fit. Attention should be paid to the measurement of the hand in dynamic positions. [ABSTRACT FROM AUTHOR]

In rock climbing, climbers use their arms to regulate their posture on the wall, which can lead to localised muscle fatigue. Evidence shows fatigue is the primary cause of falls, but little is known about how fatigue specifically affects climbing rhythm and hand movements. The present study examined climbing fluidity and hand movements on an indoor climbing wall before and after a specific fatiguing protocol. Seventeen climbers completed three repetitions of a challenging climbing route (21 on Ewbank scale) with different levels of localised arm fatigue. Climbers’ movements were tracked using 3D motion capture, and their hand actions assessed using notational analysis. Seventy markers were used to create 15 rigid body segments and the participants' centre of mass. The global entropy index was calculated on the path of the participants' centre of mass. Climbers fell more often when fatigued, but there were no significant differences in hip jerk or global entropy index when fatigued. No significant differences were found between the number of exploratory or performatory hand movements with different amounts of fatigue. The results suggest that localised arm fatigue affects a climber’s ability to prevent themselves from falling, but it does not specifically affect their fluidity. [ABSTRACT FROM AUTHOR]

Information about the position of our hand is provided by multisensory signals that are often not perfectly aligned. Discrepancies between the seen and felt hand position or its movement trajectory engage the processes of 1) multisensory integration, 2) sensory recalibration, and 3) motor adaptation, which adjust perception and behavioral responses to apparently discrepant signals. To foster our understanding of the coemergence of these three processes, we probed their short-term dependence on multisensory discrepancies in a visuomotor task that has served as a model for multisensory perception and motor control previously. We found that the well-established integration of discrepant visual and proprioceptive signals is tied to the immediate discrepancy and independent of the outcome of the integration of discrepant signals in immediately preceding trials. However, the strength of integration was context dependent, being stronger in an experiment featuring stimuli that covered a smaller range of visuomotor discrepancies (±15°) compared with one covering a larger range (±30°). Both sensory recalibration and motor adaptation for nonrepeated movement directions were absent after two bimodal trials with same or opposite visuomotor discrepancies. Hence our results suggest that short-term sensory recalibration and motor adaptation are not an obligatory consequence of the integration of preceding discrepant multisensory signals. [ABSTRACT FROM AUTHOR]

Background: With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. Objective: We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. Methods: A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test—developed by our group—where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. Results: In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t 49=3.45; P =.004), lower proportion of fixation duration (t 49=2.69; P =.04), longer time to completion (t 49=–3.44; P =.004), and a greater number of errors (t 49=–3.77; P =.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F 1-score. Conclusions: Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach. [ABSTRACT FROM AUTHOR]

Brain activity was recorded using a whole-head magnetoencephalography system followed by coherence analysis to assess neural connectivity in 10 healthy right-handed adults to clarify differences in neural connectivity in brain regions during action observation from several perspectives. The subjects were instructed to observe and memorize or imitate the hand action from a first-person or second-person visual perspective. The brain activity in coherence was modified among frontal and central, sensorimotor, and mirror neuron system-related regions based on the visual perspectives of finger movements. The regional activity in coherence changed similarly under the imitation and observation tasks compared with the condition of observing static hand figures. The information from different visual perspectives of body movements was processed in the frontal-central regions related to sensorimotor processes and partially in mirror neuron system. [ABSTRACT FROM AUTHOR]

Purpose of this work is the analysis of the hemodynamic response to the execution of various types of movements (single movement, series of movements, “tapping”) by the right hand. Methods. In this paper, the hemodynamic response was recorded using functional near infrared spectroscopy (NIRScout instrument from NIRx, Germany). The NIRScout system uses 16 optodes (8 sources and 8 detectors) to record the hemodynamic response in the cerebral cortex with a sampling rate of 7.8125 Hz. Optodes are non-invasively placed on the patient’s scalp by inserting into the sockets of a special cap “EASYCAP”. Results. We show that the total hemodynamic response in the motor cortex of the left hemisphere slightly differs between all the considered types of movement, while the severity of contralaterality demonstrates significant differences between the types of movements. Contralaterality is most pronounced when performing a series of movements, while a single squeeze of the hand causes the least contralaterality. Conclusion. The results obtained in this paper demonstrate the high sensitivity of functional near-infrared spectroscopy technology to the performance of various types of movements. It should be especially noted here short single hand squeezes, which are clearly visible on the characteristics of HbO and HbR, which can be used in the development and design of various brain – computer interfaces, including multimodal ones.

Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.

The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement’s continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson’s correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement’s kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas.

We study an individual’s propensity for rational thinking; the avoidance of cognitive biases (unconscious errors generated by our mental simplification methods) using a novel augmented reality (AR) platform. Specifically, we developed an odd-one-out (OOO) game-like task in AR designed to try to induce and assess confirmatory biases. Forty students completed the AR task in the laboratory, and the short form of the comprehensive assessment of rational thinking (CART) online via the Qualtrics platform. We demonstrate that behavioural markers (based on eye, hand and head movements) can be associated (linear regression) with the short CART score – more rational thinkers have slower head and hand movements and faster gaze movements in the second more ambiguous round of the OOO task. Furthermore, short CART scores can be associated with the change in behaviour between two rounds of the OOO task (one less and one more ambiguous) – hand-eye-head coordination patterns of the more rational thinkers are more consistent in the two rounds. Overall, we demonstrate the benefits of augmenting eye-tracking recordings with additional data modalities when trying to understand complicated behaviours.

Previous studies have revealed drastic changes in motor processing in individuals with congenital or acquired limb deficiencies and dysfunction. However, little is known about whether their brains also exhibit characteristic proprioceptive processing. Using functional magnetic resonance imaging, we examined the brain activity characteristics of four individuals with congenital or acquired paraplegia (paraplegic group) who underwent long-term wheelchair sports training, when they passively experienced a right-hand movement (passive task) and when they actively performed a right-hand motor task (active task), compared to 37 able-bodied individuals (control group). Compared with the control group, the paraplegic group showed significantly greater activity in the foot section of the left primary motor cortex and in the inferior frontoparietal proprioceptive network during the passive task. In the paraplegic group, the left intraparietal sulcus region was activated during the passive task, but suppressed during the active task, which was not observed in the control group. This shows the facilitation of hand proprioceptive processing and unique usage of the intraparietal sulcus region in proprioceptive motor processing in the brains of paraplegic individuals with long-term wheelchair sports training. [ABSTRACT FROM AUTHOR]

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains. [ABSTRACT FROM AUTHOR]

Learning to write involves integrating motor production and visual perception to develop orthographic representations. This study tries to test the effect of hand movement training as a pathway to neural correlates for L2 Chinese and L2 English readers. Twenty L2 Chinese and 20 L2 English (n = 20) adults participated in both behavioral and electroencephalogram (EEG) experiments. We designed six learning conditions: Hand Writing Chinese (HC), Viewing Chinese (VC), Drawing followed by Character Recognition in Chinese (DC), Hand Writing English (HE), Viewing English (VE), and Drawing followed by Word Recognition in English (DE). Behavioral and EEG results demonstrated that drawing facilitated visual word recognition in Chinese compared to viewing. The findings imply that hand movement could strengthen the neural processing and improve behavioral performance in Chinese character recognition for L2 Chinese learners and English word recognition for L2 Chinese learners. Furthermore, N170 amplitude at the drawing condition was positively correlated with N400 amplitudes. Thus, the early visual word recognition neural indicator (e.g., N170) was predictive of the late neural indicator of semantic processing (e.g., N400), suggesting that hand movement facilitates the neural correlates between early word recognition and later comprehension. [ABSTRACT FROM AUTHOR]

The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement’s continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson’s correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement’s kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals. [ABSTRACT FROM AUTHOR]

Moving the hands or chewing in the encoding stage enhances memory, because body movement activates the frontal cortex, which is crucial to the memory process. However, how hand movement facilitates word memory in an applied setting and whether it produces long-term effects remain unclear. Grade 1 students studied 15 new words through different strategies: fun hand movement, verbal repetition, listening (Study 1), copying words, and pure hand movement (Study 2). They recalled the words immediately, 25 minutes later, and 3 days later. Their memory performance was the best under the pure hand movement condition and the poorest under the verbal repetition and listening conditions. Moreover, the 3-day delayed recall was similar to the immediate recall under the pure hand movement condition, whereas recall decreased after 3 days in other conditions. These findings demonstrate effective strategies of word memory for vocabulary learning in classroom settings. [ABSTRACT FROM AUTHOR]

Human movements vary on trial-by-trial due to signal-dependent noise that is added to motor commands and provokes errors of movements. In some motor tasks, humans have to produce precise movements with overcoming of the noise. This study aimed at considering the motor criteria taking into account movement errors. Three factors affecting movement errors in arm repetitive movements were investigated: the viscous force field, hand laterality, and signal-dependent noise. In the measurement experiment, the participants were asked to move their right and left hands between the target areas specified as the motor task. Pseudo null, positive (resist), and negative (assist) viscous force fields were set in the workspace of the motor task. A two-way analysis of variance involving two factors, hand (right and left) and viscosity (null, resist, and assist), in the movement error was conducted, and it revealed significant main effects of hand and viscosity, and a significant interaction between them. The data obtained from the measurement suggested consistent characteristics for the shape of the paths of the movement trajectories. A simulation experiment was conducted to investigate the shape characteristics which are regarded as the nature of the human motor control system. Under the assumption of signal-dependent noise, results of the experiment revealed that the shape characteristics are associated with lower variance of a movement error directly relating the task achievement rather than an error of a movement endpoint. The lower variance of the movement error is of greater significance as the criteria of motor planning compared to the smoothness of the movement.

Learning to write involves integrating motor production and visual perception to develop orthographic representations. This study tries to test the effect of hand movement training as a pathway to neural correlates for L2 Chinese and L2 English readers. Twenty L2 Chinese and 20 L2 English (n = 20) adults participated in both behavioral and electroencephalogram (EEG) experiments. We designed six learning conditions: Hand Writing Chinese (HC), Viewing Chinese (VC), Drawing followed by Character Recognition in Chinese (DC), Hand Writing English (HE), Viewing English (VE), and Drawing followed by Word Recognition in English (DE). Behavioral and EEG results demonstrated that drawing facilitated visual word recognition in Chinese compared to viewing. The findings imply that hand movement could strengthen the neural processing and improve behavioral performance in Chinese character recognition for L2 Chinese learners and English word recognition for L2 Chinese learners. Furthermore, N170 amplitude at the drawing condition was positively correlated with N400 amplitudes. Thus, the early visual word recognition neural indicator (e.g., N170) was predictive of the late neural indicator of semantic processing (e.g., N400), suggesting that hand movement facilitates the neural correlates between early word recognition and later comprehension.

The human brain has the capacity to drastically alter its somatotopic representations in response to congenital or acquired limb deficiencies and dysfunctions. The main purpose of the present study was to elucidate such extreme adaptability in the brain of an active top wheelchair racing Paralympian (participant P1) who has congenital paraplegia (dysfunction of bilateral lower limbs). Participant P1 has undergone long-term wheelchair racing training using bilateral upper limbs and has won a total of 19 medals in six consecutive summer Paralympic games as of 2021. We examined the functional and structural changes in the foot section of the primary motor cortex (M1) in participant P1 as compared to able-bodied control participants. We also examined the functional and structural changes in three other individuals (participants P2, P3, and P4) with acquired paraplegia, who also had long-term non-use period of the lower limbs and had undergone long-term training for wheelchair sports (but not top athletes at the level of participant P1). We measured brain activity in all the participants using functional magnetic resonance imaging (MRI) when bimanual wrist extension-flexion movement was performed, and the structural MRI images were collected. Compared to 37 control participants, participant P1 showed significantly greater activity in the M1 foot section during the bimanual task, and significant local GM expansion in this section. Significantly greater activity in the M1 foot section was also observed in participant P4, but not in P2 and P3, and the significant local GM expansion was observed in participant P2, but not in P3 and P4. Thus, functional or structural change was observed in an acquired paraplegic participant, but was not observed in all the paraplegic participants. The functional and structural changes typically observed in participant P1 may represent extreme adaptability of the human brain. We discuss the results in terms of a new idea of hyper-adaptation. [ABSTRACT FROM AUTHOR]

The human brain has the capacity to drastically alter its somatotopic representations in response to congenital or acquired limb deficiencies and dysfunctions. The main purpose of the present study was to elucidate such extreme adaptability in the brain of an active top wheelchair racing Paralympian (participant P1) who has congenital paraplegia (dysfunction of bilateral lower limbs). Participant P1 has undergone long-term wheelchair racing training using bilateral upper limbs and has won a total of 19 medals in six consecutive summer Paralympic games as of 2021. We examined the functional and structural changes in the foot section of the primary motor cortex (M1) in participant P1 as compared to able-bodied control participants. We also examined the functional and structural changes in three other individuals (participants P2, P3, and P4) with acquired paraplegia, who also had long-term non-use period of the lower limbs and had undergone long-term training for wheelchair sports (but not top athletes at the level of participant P1). We measured brain activity in all the participants using functional magnetic resonance imaging (MRI) when bimanual wrist extension-flexion movement was performed, and the structural MRI images were collected. Compared to 37 control participants, participant P1 showed significantly greater activity in the M1 foot section during the bimanual task, and significant local GM expansion in this section. Significantly greater activity in the M1 foot section was also observed in participant P4, but not in P2 and P3, and the significant local GM expansion was observed in participant P2, but not in P3 and P4. Thus, functional or structural change was observed in an acquired paraplegic participant, but was not observed in all the paraplegic participants. The functional and structural changes typically observed in participant P1 may represent extreme adaptability of the human brain. We discuss the results in terms of a new idea of hyper-adaptation.

Conventional rehabilitation strategies for stroke survivors become difficult when voluntary movements are severely disturbed. Combining passive limb mobilization, robotic devices and EEG-based brain-computer interfaces (BCI) systems might improve treatment and clinical follow-up of these patients, but detailed knowledge of neurophysiological mechanisms involved in functional recovery, which might help for tailoring stroke treatment strategies, is lacking. Movement-related EEG changes (EEG event-related desynchronization (ERD) in b e t a and a l p h a bands, an indicator of motor cortex activation traditionally used for BCI systems), were evaluated in a group of 23 paralyzed chronic stroke patients in two unilateral motor tasks alternating paretic and healthy hands ((i) passive movement, using a hand exoskeleton, and (ii) voluntary movement), and compared to nine healthy subjects. In tasks using unaffected hand, we observed an increase of contralesional hemisphere activation for stroke patients group. Unexpectedly, when using paralyzed hand, motor cortex activation was reduced or absent in severely affected group of patients, while patients with moderate motor deficit showed an activation greater than control group. Cortical activation was reduced or absent in damaged hemisphere of all the patients in both tasks. Significant differences related to severity of motor deficit were found in the time course of a l p h a - b e t a bands power ratio in EEG of contralesional hemisphere while moving affected hand. These findings suggest the presence of different compensation mechanisms in contralesional hemisphere of stroke patients related to the grade of motor disability, that might turn quantitative EEG during a movement task, obtained from a BCI system controlling a robotic device included in a rehabilitation task, into a valuable tool for monitoring clinical progression, evaluating recovery, and tailoring treatment of stroke patients. [ABSTRACT FROM AUTHOR]

Introduction: The neural response is a noisy random process. The neural response to a sensory stimulus is completely equivalent to a list of spike times in the spike train. In previous studies, decreased neuronal response variability was observed in the cortex’s various areas during motor preparatory in reaching tasks. The reasons for the reduction in Neural Variability (NV) are unclear. It could be influenced by an increased firing rate, or it could result from the intrinsic characteristic of cells during the Reaction Time (RT). Methods: A neural response function with an underlying deterministic instantaneous firing rate signal and a random Poisson process spike generator was simulated in this research. Neural stimulation could help us understand the relationships between the complex data structures of cortical activities and their stability in detail during motor intention in arm-reaching tasks. Results: Our measurements indicated a similar pattern of results to the cortex, a sharp reduction of the normalized variance of simulated spike trains across all trials. We also observed a reverse relationship between activity and normalized variance. Conclusion: The present study findings could be applied to neural engineering and brain-machine interfaces for controlling external devices, like the movement of a robot arm.

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.

Recent evidence suggests that target-relevant sensory stimuli (i.e., visual, auditory, and olfactory) can play important roles in the motor system. However, little is known about the effects of olfactory information on reaching and grasping movements. To determine whether odor stimuli affect hand movements, the reaching and grasping kinematic characteristics of 29 human participants were recorded using a three-dimensional video motion capture system. Participants received an odor stimulus by Sniffin’ Sticks and then reached toward and grasped a target. Grasping targets were apple, orange, ginger, and garlic. The odor stimulus was congruent with the target. The size of the odor-cued object (OCO) was the same size, smaller, or larger than a target to be grasped; or participants received odorless air while they viewed that target. They reached the target with one of two grips: a precision grip for a small target or a power grip for a larger target. The visual feedback was lost in half of 80 total trials after a start signal. It was no longer visible when participants reached the target. The results of repeated-measures analyses of variance followed by simple-effects analyses showed that when the size of the hand movement evoked by the odor cue was congruent with the size of the target, either both small or both large, the reaction time was significantly shorter than it was for odorless air. When participants received visual feedback throughout the trial, movement duration was significantly shorter if the odor cue was congruent with the size of the target or if odorless air was dispensed. When the size of hand movement evoked by the odor cue was incongruent with the size of the target, an interference effect was apparent on the maximum aperture time. The result of odorless air control group in a closed loop was shorter than incongruent odor group. In addition, visual feedback influenced the results such that the maximum aperture time occurred later when visibility was blocked only in the odorless air control condition. These results suggest that olfactory information has a positive effect on reach-to-grasp hand movements and that vision and olfaction may interact to optimize motor behavior.