Relevant bibliographies by topics / Motor imagery

Academic literature on the topic 'Motor imagery'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Motor imagery"

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Sharma, Nikhil, Valerie M. Pomeroy, and Jean-Claude Baron. "Motor Imagery." Stroke 37, no. 7 (July 2006): 1941–52. http://dx.doi.org/10.1161/01.str.0000226902.43357.fc.

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Lotze, Martin, and Ulrike Halsband. "Motor imagery." Journal of Physiology-Paris 99, no. 4-6 (June 2006): 386–95. http://dx.doi.org/10.1016/j.jphysparis.2006.03.012.

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Livesey, D. J., and M. Kangas. "The Role of Visual Movement Imagery in Kinaesthetic Sensitivity and Motor Performance." Australian Educational and Developmental Psychologist 14, no. 1 (May 1997): 2–10. http://dx.doi.org/10.1017/s0816512200027607.

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ABSTRACTThe relationship between vividness of visual movement imagery and performance on tests of kinaesthetic sensitivity was examined in high school students by comparing performance on three tests of kinaesthesis by high and low imagery students, selected using the Vividness of Movement Imagery Questionnaire. High imagers performed significantly better than low imagers when relying on kinaesthetic information. Level of movement imagery predicted performance on a motor task (a manual placement task) when the task was performed in the absence of visual cues (blindfolded). These results reflect the reliance on visual information when performing motor tasks and indicate that, in the absence of visual cues, such information is created from kinaesthetic input via visual imagery. This has important implications for our understanding of the development of kinaesthesis and motor control and may contribute to the development of remedial programmes for children with poor motor ability.
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Priganc, Victoria W., and Susan W. Stralka. "Graded Motor Imagery." Journal of Hand Therapy 24, no. 2 (April 2011): 164–69. http://dx.doi.org/10.1016/j.jht.2010.11.002.

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Zhang, Lanlan, Yanling Pi, Hua Zhu, Cheng Shen, Jian Zhang, and Yin Wu. "Motor experience with a sport-specific implement affects motor imagery." PeerJ 6 (April 27, 2018): e4687. http://dx.doi.org/10.7717/peerj.4687.

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The present study tested whether sport-specific implements facilitate motor imagery, whereas nonspecific implements disrupt motor imagery. We asked a group of basketball players (experts) and a group of healthy controls (novices) to physically perform (motor execution) and mentally simulate (motor imagery) basketball throws. Subjects produced motor imagery when they were holding a basketball, a volleyball, or nothing. Motor imagery performance was measured by temporal congruence, which is the correspondence between imagery and execution times estimated as (imagery time minus execution time) divided by (imagery time plus execution time), as well as the vividness of motor imagery. Results showed that experts produced greater temporal congruence and vividness of kinesthetic imagery while holding a basketball compared to when they were holding nothing, suggesting a facilitation effect from sport-specific implements. In contrast, experts produced lower temporal congruence and vividness of kinesthetic imagery while holding a volleyball compared to when they were holding nothing, suggesting the interference effect of nonspecific implements. Furthermore, we found a negative correlation between temporal congruence and the vividness of kinesthetic imagery in experts while holding a basketball. On the contrary, the implement manipulation did not modulate the temporal congruence of novices. Our findings suggest that motor representation in experts is built on motor experience associated with specific-implement use and thus was subjected to modulation of the implement held. We conclude that sport-specific implements facilitate motor imagery, whereas nonspecific implements could disrupt motor representation in experts.
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Nugraha, Made Hendra Satria. "MOTOR IMAGERY, ACTION OBSERVATION, DAN GRADED MOTOR IMAGERY PADA REHABILITASI STROKE." Majalah Kedokteran Neurosains Perhimpunan Dokter Spesialis Saraf Indonesia 39, no. 1 (December 20, 2021): 41–49. http://dx.doi.org/10.52386/neurona.v39i1.199.

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Stroke merupakan salah satu penyebab utama kecacatan pada orang dewasa. Mirror neuron system (MNS) dianggap sebagai terobosan besar untuk ilmu saraf dan merupakan salah satu fitur penting pada evolusi otak manusia. Penelitian ini merupakan tinjauan pustaka dengan sumber data sekunder berupa kumpulan artikel ilmiah yang diakses melalui journal database, seperti: PubMed Central (PMC) NCBI dan google scholar. Kajian pustaka ini bertujuan untuk: (1) mengetahui efektivitas MI, AO, dan GMI dalam memperbaiki gerak dan fungsi tubuh pada pasien stroke serta (2) memahami protokol penatalaksanaan MI, AO, dan GMI dalam memperbaiki gerak dan fungsi tubuh pada pasien stroke. Hasil kajian pustaka menunjukkan bahwa intervensi MI, AO, dan GMI efektif dalam memperbaiki fungsi dan gerak tubuh saat rehabilitasi stroke. Protokol penatalaksanaan MI, AO, dan GMI memiliki variasi dilihat dari segi frekuensi, intensitas, dan durasi terapi, dimana sebagian besar pemberian intervensi ini dapat menunjukkan manfaat yang lebih baik jika dikombinasikan dengan intervensi konvensional fisioterapi lainnya.
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Klatzky, Roberta L. "On the relation between motor imagery and visual imagery." Behavioral and Brain Sciences 17, no. 2 (June 1994): 212–13. http://dx.doi.org/10.1017/s0140525x00034178.

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Jeannerod's target article describes support, through empirical and neurological findings, for the intriguing idea of motor imagery, a form of representation hypothesized to have levels of functional equivalence with motor preparation, while being consciously accessible. Jeannerod suggests that the subjectively accessible content of motor imagery allows it to be distinguished from motor preparation, which is unconscious. Motor imagery is distinguished from visual imagery in terms of content. Motor images are kinesthetic in nature; they are parametrized by variables such as force and time and they are potentially governed by kinematic rules. Jeannerod acknowledges, however, that motor and visual imagery may not easily be separated, because actions take place in a spatial environment. I agree; in fact, I suggest here that visualization may generally be concomitant with, and may even subjectively dominate, motor imagery.
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Sawai, Shun, Shoya Fujikawa, Ryu Ushio, Kosuke Tamura, Chihiro Ohsumi, Ryosuke Yamamoto, Shin Murata, and Hideki Nakano. "Repetitive Peripheral Magnetic Stimulation Combined with Motor Imagery Changes Resting-State EEG Activity: A Randomized Controlled Trial." Brain Sciences 12, no. 11 (November 15, 2022): 1548. http://dx.doi.org/10.3390/brainsci12111548.

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Repetitive peripheral magnetic stimulation is a novel non-invasive technique for applying repetitive magnetic stimulation to the peripheral nerves and muscles. Contrarily, a person imagines that he/she is exercising during motor imagery. Resting-state electroencephalography can evaluate the ability of motor imagery; however, the effects of motor imagery and repetitive peripheral magnetic stimulation on resting-state electroencephalography are unknown. We examined the effects of motor imagery and repetitive peripheral magnetic stimulation on the vividness of motor imagery and resting-state electroencephalography. The participants were divided into a motor imagery group and motor imagery and repetitive peripheral magnetic stimulation group. They performed 60 motor imagery tasks involving wrist dorsiflexion movement. In the motor imagery and repetitive peripheral magnetic stimulation group, we applied repetitive peripheral magnetic stimulation to the extensor carpi radialis longus muscle during motor imagery. We measured the vividness of motor imagery and resting-state electroencephalography before and after the task. Both groups displayed a significant increase in the vividness of motor imagery. The motor imagery and repetitive peripheral magnetic stimulation group exhibited increased β activity in the anterior cingulate cortex by source localization for electroencephalography. Hence, combined motor imagery and repetitive peripheral magnetic stimulation changes the resting-state electroencephalography activity and may promote motor imagery.
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Fontani, Giuliano, Silvia Migliorini, Leda Lodi, Enrico De Martino, Nektarios Solidakis, and Fausto Corradeschi. "Internal–External Motor Imagery and Skilled Motor Actions." Journal of Imagery Research in Sport and Physical Activity 9, no. 1 (January 1, 2014): 1–11. http://dx.doi.org/10.1515/jirspa-2012-0001.

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AbstractThe purpose of this study was to analyze the movement-related brain macropotentials (MRBMs) recorded during the execution of two tests of motor imagery: kinaesthetic (internal) and visual (external). Recordings were compared with those obtained performing a GO/NOGO motor test. The GO test required pressure of three keys of a modified keyboard in sequence when a figure appeared in the computer screen. On NOGO trials no button had to be pressed. Motor imagery tests were an internal or kinaesthetic imagination test (IN MI) on which participants imagined performing the pressure of keyboard buttons, avoiding any real movement, and an external or visual imagination test (EX MI) on which subjects were asked to imagine seeing their finger press the buttons. With the completion of the Movement Imagery Questionnaire, the participants were assigned into two groups: high (11) and low (10) capacity of imagination. The results showed an increase in the amplitude of the MRBMs wave occurring in the prestimulus period of imagination, with respect to real motor action. In the poststimulus period, the amplitude and duration of the waves recorded during motor action were higher than those recorded during the motor imagery tests. The comparison between EX and IN MI showed a lower latency and a higher amplitude of the brain waves recorded during internal motor imagery with respect to those observed during EX MI. The experimental data confirm that real motor activity is related to higher amplitude MRBMs than motor imagery. The profile of the waves recorded during internal imagery seems to be related to a higher brain involvement compared to those recorded during external visual imagery; it suggest that the kinaesthetic process of imagination is more efficient in information processing and motor skill acquisition.
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Dickstein, Ruth, and Judith E. Deutsch. "Motor Imagery in Physical Therapist Practice." Physical Therapy 87, no. 7 (July 1, 2007): 942–53. http://dx.doi.org/10.2522/ptj.20060331.

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Motor imagery is the mental representation of movement without any body movement. Abundant evidence on the positive effects of motor imagery practice on motor performance and learning in athletes, people who are healthy, and people with neurological conditions (eg, stroke, spinal cord injury, Parkinson disease) has been published. The purpose of this update is to synthesize the relevant literature about motor imagery in order to facilitate its integration into physical therapist practice. This update also will discuss visual and kinesthetic motor imagery, factors that modify motor imagery practice, the design of motor imagery protocols, and potential applications of motor imagery.
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Dissertations / Theses on the topic "Motor imagery"

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Hovington, Cindy. "Motor imagery : does strategy matter?" Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1369.

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Bovend'Eerdt, Thamar J. H. "Motor Imagery in Neurological Rehabilitation." Thesis, Oxford Brookes University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520927.

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BONASSI, GAIA. "Motor imagery and motor illusion: from plasticity to a translational approach." Doctoral thesis, Università degli studi di Genova, 2018. http://hdl.handle.net/11567/929823.

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Williams, Jacqueline Louise, and jacqueline williams@mcri edu au. "Motor imagery and developmental coordination disorder (DCD)." RMIT University. Health Sciences, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080617.141139.

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Developmental Coordination Disorder (DCD) is characterised by impairments to motor control and learning, the cause of which remains unclear. Recently, researchers have used cognitive neuroscientific approaches to explore the basis of poor coordination in children, with one hypothesis suggesting that an internal modelling deficit (IMD) is one of the underlying causes of DCD. The aim of this thesis was to further test the IMD hypothesis using a motor imagery paradigm - the mental rotation of hands. Versions of this task were used in all studies to assess motor imagery ability, with an additional whole-body task used in Studies 2 and 3. Further, an alphanumeric rotation task was used in Studies 1 and 2 to assess visual imagery ability. Studies 1 and 2 provided varying levels of support for the IMD hypothesis. In Study 1, only a subgroup of DCD children performed differently from other children in the study on the hand tasks, but tighter task constraints in Study 2 led to overall group differences between DCD and controls in terms of accuracy. The DCD group were also significantly less accurate than controls in the whole-body task, but there were no group differences in either Study 1 or 2 on the visual imagery task. Interestingly, in Study 2, there was an indication that children with severe levels of motor impairment were less accurate than children with less severe motor impairment, suggesting that motor impairment level could play a role in the severity of motor imagery deficits. Study 3 was designed to explore the impact of motor impairment severity on motor imagery ability further. The results confirmed that children with severe DCD had greater motor imagery impairment than children with mild DCD - children with severe DCD performed less accurately than both controls and those with mild DCD in the hand task with instructions and the controls in the whole-body task. Further, those children with mild DCD were able to respond somewhat to motor imagery instructions, whereas those with severe DCD were not. This study provided support to the IMD hypothesis, though the deficit was shown to be dependent on a number of factors. Chapter 5 presents a reasoned account of these various findings and their implications are discussed. It is concluded that motor imagery deficits are evident in many children with DCD, but more so in children with severe motor impairment. A general imagery deficit was ruled out based on the findings of Studies 1 and 2 which showed that visual imagery processes appear intact in children with DCD. Taken together with previous imagery and IMD studies, and related research on feedforward control in DCD, it is concluded that the deficits in motor imagery observed in this thesis are consistent with the hypothesis that an IMD is one likely causal factor in the disorder, particularly in more severe DCD. The observation of differing response patterns between children with mild and severe forms of DCD has important implications for developing a theory of DCD and for remediation.
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Schuster, Corina. "Motor imagery techniques applied in stroke rehabilitation." Thesis, Oxford Brookes University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.579510.

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Background: Motor imagery (MI) has been shown to be beneficial if added to physical practice. It remained unclear, whether M I is effective in patients after stroke, whether MI techniques differ across disciplines, and whether MI can be applied to complex motor tasks (MTs) in patients after stroke. Methods and results: Two systematic reviews were conducted: firstly, to evaluate evidence of MI interventions. Four randomised controlled trials (RCTs) confirmed MI efficacy in patients after stroke if added to therapy. Secondly, characteristics of successful MI training sessions in different disciplines were reviewed. Totally 141 MI interventions were identified in education, medicine, music, psychology, and sports. Information describing 17 MI training elements and 7 temporal parameters were identified and compared. Prior to conducting a pilot RCT, two questionnaires to assess MI ability (KVIQ, Imaprax) were translated with associated validity and reliability testing. The single blinded pilot RCT compared two MI approaches: embedded (n=13) vs. added (N=13) MI vs. a control group (N=14) in patients after stroke. Primary outcome measure was time to perform a complex MT. Results revealed a significant change for all three groups from pre- to post-intervention but no group differences. A qualitative study evaluated MI experiences in patients from experimental groups using semi-structured interviews. Results showed that answers matched to MI framework questions where, when, what, why, and how to use imagery. Conclusions and contributions: MI is still under-researched in stroke rehabilitation. Conducted research showed that MI was beneficial if added to therapy and MI techniques varied across disciplines. Embedded and added MI supported patients similarly and could be applied to a complex MT. MI appeared spontaneously in patients after stroke and was used to practice simple movements. Furthermore, this thesis proposed steps towards consistent term usage and detailed MI intervention reporting, which is lacking in current Ml literature.
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Bialko, Christopher Stephen. "The Effects of Practice and Load on Actual and Imagined Action." Cleveland State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=csu1242884385.

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RAMOS, ALIMED CELECIA. "MULTIPLE CLASSIFIER SYSTEM FOR MOTOR IMAGERY TASK CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30903@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Interfaces Cérebro Computador (BCIs) são sistemas artificiais que permitem a interação entre a pessoa e seu ambiente empregando a tradução de sinais elétricos cerebrais como controle para qualquer dispositivo externo. Um Sistema de neuroreabilitação baseado em EEG pode combinar portabilidade e baixo custo com boa resolução temporal e nenhum risco para a vida do usuário. Este sistema pode estimular a plasticidade cerebral, desde que ofereça confiabilidade no reconhecimento das tarefas de imaginação motora realizadas pelo usuário. Portanto, o objetivo deste trabalho é o projeto de um sistema de aprendizado de máquinas que, baseado no sinal de EEG de somente dois eletrodos, C3 e C4, consiga classificar tarefas de imaginação motora com alta acurácia, robustez às variações do sinal entre experimentos e entre sujeitos, e tempo de processamento razoável. O sistema de aprendizado de máquina proposto é composto de quatro etapas principais: pré-processamento, extração de atributos, seleção de atributos, e classificação. O pré-processamento e extração de atributos são implementados mediante a extração de atributos estatísticos, de potência e de fase das sub-bandas de frequência obtidas utilizando a Wavelet Packet Decomposition. Já a seleção de atributos é efetuada por um Algoritmo Genético e o modelo de classificação é constituído por um Sistema de Múltiplos Classificadores, composto por diferentes classificadores, e combinados por uma rede neural Multi-Layer Perceptron. O sistema foi testado em seis sujeitos de bases de dados obtidas das Competições de BCIs e comparados com trabalhos benchmark da literatura, superando os resultados dos outros métodos. Adicionalmente, um sistema real de BCI para neurorehabilitação foi projetado, desenvolvido e testado, produzindo também bons resultados.
Brain Computer Interfaces (BCIs) are artificial systems that allow the interaction between a person and their environment using the translated brain electrical signals to control any external device. An EEG neurorehabilitation system can combine portability and affordability with good temporal resolution and no health risks to the user. This system can stimulate the brain plasticity, provided that the system offers reliability on the recognition of the motor imagery (MI) tasks performed by the user. Therefore, the aim of this work is the design of a machine learning system that, based on the EEG signal from only C3 and C4 electrodes, can classify MI tasks with high accuracy, robustness to trial and inter-subject signal variations, and reasonable processing time. The proposed machine learning system has four main stages: preprocessing, feature extraction, feature selection, and classification. The preprocessing and feature extraction are implemented by the extraction of statistical, power and phase features of the frequency sub-bands obtained by the Wavelet Packet Decomposition. The feature selection process is effectuated by a Genetic Algorithm and the classifier model is constituted by a Multiple Classifier System composed by different classifiers and combined by a Multilayer Perceptron Neural Network as meta-classifier. The system is tested on six subjects from datasets offered by the BCIs Competitions and compared with benchmark works founded in the literature, outperforming the other methods. In addition, a real BCI system for neurorehabilitation is designed and tested, producing good results as well.
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White, Alison Elizabeth. "Imagery and sport performance." Thesis, Bangor University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320414.

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Bolles, Gina. "An Exploratory study : the intersection of imagery ability, imagery use, and learning style /." Connect to title online (Scholars' Bank), 2008. http://hdl.handle.net/1794/7478.

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Ammar, Diala Fouad. "The role (relationship) of visual and motor imagery in estimating reach." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/5992.

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The primary intent of this study was to explore fundamental questions about the role and relationship between motor (MI) and visual (VI) imagery within the context of estimating reach. Experiment 1 examined and compared VI and MI tasks under matched environmental conditions with the intent to explore the distinction and cooperation of the visual and motor systems in representing actions. The design of this experiment included an interference paradigm modified from Stevens (2005) in which six blocks of trials (conditions) were used: MI, VI, MI with visual interference, MI with motor interference, VI with motor interference, and VI with visual interference. Results indicated that MI was significantly more accurate than VI in regard to total error, distribution of error and mean bias (p <= .05). Significant increases in the number of errors and estimation bias were found when the modalities for the imagined task and the interference task were matched. The data showed that motor tasks interfered with the ability to MI, whereas visual tasks interfered with the ability to VI. Experiment 2 included a response-delay paradigm modified from Bradshaw and Watt (2002) in which eight blocks of trials were used: MI and VI conditions with no-delay and delays of 1-, 2- and 4 s. Overall, this experiment demonstrated that response-delay influenced accuracy of the MI (visuomotor) task, but not the VI (perceptual) task. That is, after a 4s delay, error in MI increased significantly. Interestingly, these results may indicate a crucial temporal constraint for the representation of distance, isolated in the visuomotor system. In view of both experiments, the findings are consistent with the notion of a distinction between vision for perception (VI) and vision for action (MI) as advanced by Goodale, Westwood & Milner (2004). In conclusion, VI seems to delineate relevant spatial parameters within the environment and then transfer the information to MI. At this point, information is computed in terms of biomechanical possibilities for a certain movement. In summary, just as perception and action are firmly linked, so too are MI and VI.
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Books on the topic "Motor imagery"

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John, Annett, ed. Imagery and motor processes. Leicester: The British Psychological Society, 1995.

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Guillot, Aymeric. The neurophysiological foundations of mental and motor imagery. Oxford: Oxford University Press, 2010.

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Evans, Sandra Elisabeth. Sources of variation in the relationship between imagery and motor performance. Birmingham: University of Birmingham, 1989.

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Numminen, Pirkko. The role of imagery in physical education. Jyväskylä: University of Jyväskylä, 1991.

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Abdulgabbar, Adel S. The effect of imagery ability on imitation of a closed-motor task. [s.l.]: typescript, 1990.

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Burstein, Dennis. The effects of using video-imagery fusion in learning swimming skills. Eugene: Microform Publications, College of Human Development and Performance, University of Oregon, 1987.

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Oslin, Judith L. A meta-analysis of mental practice research: Differentiation between intent and type of cognitive activity utilized. Eugene: Microform Publications, College of Human Development and Performance, University of Oregon, 1987., 1987.

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Johannes, Engelkamp, and Zimmer H. D. 1953-, eds. Memory and processing of visual and spatial information. Lengerich: Pabst Science Publishers, 1996.

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Powers, John P. Automatic particle sizing from rocket motor holograms. Monterey, Calif: Naval Postgraduate School, 1990.

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Kerbrech, Richard P. De. The Shaw Savill line: Images in mast, steam and motor. Coltishall: Ship Pictorial, 1992.

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Book chapters on the topic "Motor imagery"

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Hortal, Enrique. "Motor Imagery." In Brain-Machine Interfaces for Assistance and Rehabilitation of People with Reduced Mobility, 1–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95705-0_1.

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Anema, Helen A., and H. Chris Dijkerman. "Motor and Kinesthetic Imagery." In Multisensory Imagery, 93–113. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5879-1_6.

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Munzert, Jörn, and Britta Lorey. "Motor and Visual Imagery in Sports." In Multisensory Imagery, 319–41. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5879-1_17.

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Arpaia, Pasquale, Antonio Esposito, Ludovica Gargiulo, and Nicola Moccaldi. "Motor Imagery-Based Instrumentation." In Wearable Brain-Computer Interfaces, 171–86. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003263876-10.

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Malouin, Francine, and Carol L. Richards. "Clinical Applications of Motor Imagery in Rehabilitation." In Multisensory Imagery, 397–419. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5879-1_21.

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Bartolomeo, Paolo, Alexia Bourgeois, Clémence Bourlon, and Raffaella Migliaccio. "Visual and Motor Mental Imagery After Brain Damage." In Multisensory Imagery, 249–69. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5879-1_13.

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Mizuguchi, Nobuaki. "Brain Activity During Motor Imagery." In Sports Performance, 13–23. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55315-1_2.

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Godøy, Rolf Inge. "Intermittent Motor Control in Volitional Musical Imagery." In Music and Mental Imagery, 42–53. London: Routledge, 2022. http://dx.doi.org/10.4324/9780429330070-5.

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Kokai, Yuki, Isao Nambu, and Yasuhiro Wada. "Identifying Motor Imagery-Related Electroencephalogram Features During Motor Execution." In Neural Information Processing, 90–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_8.

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Nguyen, Phuoc, Dat Tran, Xu Huang, and Wanli Ma. "Motor Imagery EEG-Based Person Verification." In Advances in Computational Intelligence, 430–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38682-4_46.

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Conference papers on the topic "Motor imagery"

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Jiang, Lijun, Eugene Tham, Mervyn Yeo, Zaixing Wang, and Bo Jiang. "Motor imagery controlled wheelchair system." In 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2014. http://dx.doi.org/10.1109/iciea.2014.6931221.

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Choy, Chi Sang, Zixin Ye, Ziyang Huang, Qifeng Zheng, Qiang Fang, Seedahmed S. Mahmoud, Katrina Neville, and Beth Jelfs. "Motor Imagery Observed by fNIRS." In 2023 IEEE 19th International Conference on Body Sensor Networks (BSN). IEEE, 2023. http://dx.doi.org/10.1109/bsn58485.2023.10331069.

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Jung, Min-Kyung, Seho Lee, In-Nea Wang, Ha-Yoon Song, Hakseung Kim, and Dong-Joo Kim. "Phase Transition in previous Motor Imagery affects Efficiency of Motor Imagery based Brain-computer Interface." In 2021 9th International Winter Conference on Brain-Computer Interface (BCI). IEEE, 2021. http://dx.doi.org/10.1109/bci51272.2021.9385321.

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Zhang, Hang, Li Yao, and Zhiying Long. "The functional alterations associated with motor imagery training: a comparison between motor execution and motor imagery of sequential finger tapping." In SPIE Medical Imaging, edited by John B. Weaver and Robert C. Molthen. SPIE, 2011. http://dx.doi.org/10.1117/12.877346.

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Xiao, Dan, and Jianfeng Hu. "Identification of Motor Imagery EEG Signal." In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462405.

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Cososchi, Stefan, Rodica Strungaru, Alexandru Ungureanu, and Mihaela Ungureanu. "EEG Features Extraction for Motor Imagery." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260004.

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Cososchi, Stefan, Rodica Strungaru, Alexandru Ungureanu, and Mihaela Ungureanu. "EEG Features Extraction for Motor Imagery." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4397608.

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Kos'myna, Nataliya, Franck Tarpin-Bernard, and Bertrand Rivet. "Bidirectional feedback in motor imagery BCIs." In CHI '14: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2559206.2574820.

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Khan, Saadat Ullah, Muhammad Majid, and Syed Muhammad Anwar. "Motor Imagery Classification Using EEG Spectrograms." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, 2023. http://dx.doi.org/10.1109/isbi53787.2023.10230450.

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Hashmi, Athar Yawar, Bilal Alam Khan, and Anam Hashmi. "Motor Imagery Classifcation using Transfer Learning." In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2022. http://dx.doi.org/10.1109/icscds53736.2022.9761016.

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Reports on the topic "Motor imagery"

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Jiang, Linhong, Lijuan Zhao, Rui Qi, Weiqin Cong, Zhaoyuan Li, and Jianzhong Zhang. Effects of motor imagery training for lower extremity motor function in patients with stroke. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2020. http://dx.doi.org/10.37766/inplasy2020.11.0037.

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Jiang, Linhong, Lijuan Zhao, Rui Qi, Tingting Wang, and Weiqin Cong. Effects of motor imagery training for upper extremity motor function in patients with stroke of the middle recovery period : A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2020. http://dx.doi.org/10.37766/inplasy2020.10.0078.

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Slone, Scott, Marissa Torres, Alexander Stott, Ethan Thomas, and Robert Ibey. CRREL Environmental Wind Tunnel upgrades and the Snowstorm Library. Engineer Research and Development Center (U.S.), January 2024. http://dx.doi.org/10.21079/11681/48077.

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Abstract:
Environmental wind tunnels are ideal for basic research and applied physical modeling of atmospheric conditions and turbulent wind flow. The Cold Regions Research and Engineering Laboratory's own Environmental Wind Tunnel (EWT)—an open-circuit suction wind tunnel—has been historically used for snowdrift modeling. Recently the EWT has gone through several upgrades, namely the three-axis chassis motors, variable frequency drive, and probe and data acquisition systems. The upgraded wind tunnel was used to simulate various snowstorm conditions to produce a library of images for training machine learning models. Various objects and backgrounds were tested in snowy test conditions and no-snow control conditions, producing a total of 1.4 million training images. This training library can lead to improved machine learning models for image-cleanup and noise-reduction purposes for Army operations in snowy environments.
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