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1رسالة جامعية
مصطلحات موضوعية: Visual-motor illusion, Motor imagery, Sit-to-stand, Maximum effort, Sense of agency
وصف الملف: application/pdf
Degree: 博士（理学療法学） -- 東京都立大学
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2رسالة جامعية
المؤلفون: Gaur, Pramod
المساهمون: Prasad, Girijesh, Wang, Hui
مصطلحات موضوعية: 005.4, Brain Computer Interface, Motor Imagery, BCI Competition IV Dataset 2A, BCI Competition IV Dataset 2B, Riemannian Geometry, Common Spatial Pattern, Non-stationary
الوصف: This thesis focuses on the development of adaptive data-driven single channel and multichannel filtering methods for brain-computer interface (BCI) systems. Magnetoencephalography (MEG) and electroencephalogram (EEG) neuroimaging recording techniques are considered to measure neurophysiological activity. The inherent nonstationarity and nonlinearity in MEG/EEG and its multichannel recording nature require a new set of data-driven single and multichannel filtering techniques to estimate more accurately features for enhanced operation of a BCI. Empirical mode decomposition (EMD) and Multivariate EMD (MEMD) are fully data-driven adaptive techniques. These techniques are considered to decompose the nonstationary and nonlinear MEG/EEG signals into a group of components which are highly localised in the time and frequency domain. Also, it is shown that MEMD based filtering can exploit common oscillatory modes within multivariate (multichannel) data. It may be used to more accurately estimate and compare the amplitude information among multiple sources which serves as a key for the feature extraction of a BCI system. These simple filtering techniques are done at the preprocessing stage which helped to reduce the effect of the nonstationarity to a large extent across the sessions for both binary class and multi-class classification problems and identify features which are somewhat invariant against the changes across sessions. Different features such as Hjorth, bandpower, common spatial pattern (CSP), sample entropy and covariance matrix are extracted in the feature extraction stage for comparative evaluation. A novel subject specific MEMD based filtering and covariance matrix as a feature set approach is introduced to classify the multiple classes using Riemannian geometry framework. This approach helped to achieve high kappa value and classification accuracy when evaluated on BCI competition IV dataset 2a. This novel type of filtering can be applied without initial calibration and has the potential to drastically improve the applicability of BCI devices for daily use. Finally, a novel tangent space based transfer learning approach is proposed which utilizes the shared structure across multiple subjects and is an important step towards zero training time for BCI systems.
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3دورية أكاديمية
المؤلفون: Kim, Vella Shin-Hyung
مصطلحات موضوعية: Brain-Computer Interface, Pediatrics, Machine Learning, Electroencephalography, Motor Imagery, Education--Sciences, Rehabilitation and Therapy, Artificial Intelligence
الوصف: For children with severe motor impairments, brain-computer interfaces (BCIs) are a potential life changing solution that provide an alternate means of communication and control. Some BCI users experience difficulties controlling a motor-imagery BCI (MI-BCI), but the unique factors influencing BCI performance in children are largely understudied. This study aimed to build predictive models of BCI performance in typically developing children using EEG correlates, demographic factors, and subjective assessments. We also aimed to explore specific features most predictive of BCI performance in children. Two datasets, DS1 (n=31) and DS2 (n=22) from independent studies comprising of EEG data, demographic information, and subjective assessments from typically-developing children were utilized. Models were trained 15 times, each on different feature subsets from DS1 using Support Vector Machine (SVM) and Random Forest (RF) classifiers, with hyperparameter optimization conducted using a differential genetic particle ...
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4رسالة جامعية
المؤلفون: Humphries, Stacey Alexandra
المساهمون: Poliakoff, Ellen, Holler, Judith
مصطلحات موضوعية: 616.8, Parkinson's disease, Gesture, First-person perspective, Third-person perspective, Simulation, Action representation, Perspective-taking, Communication, Language, Non-verbal, Motor imagery
الوصف: Parkinson’s disease (PD) is a neurodegenerative condition which results in severe motor impairment. Deterioration in multiple domains of cognition is another hallmark of PD. Together, these motor and cognitive impairments impact substantially on language and communication. Co-speech gestures are a form of action and are also part of linguistic processes, yet have rarely been explored in PD. Gestures can provide imagistic depictions of concepts described by speech and contribute to communication in healthy individuals. They rely on visual, spatial, and motor simulations and imagery, which may be impaired in PD. It is therefore of clinical importance to evaluate how co-speech gestures might be impaired to understand the extent of communicative impairment in PD. PD can also provide a useful model to understanding the cognitive basis of co-speech gesture in healthy people. In Chapter 2, participants described isolated actions. Gesture rate did not differ between the two groups, however, the groups differed in terms of the visual perspective they adopted when depicting actions in gesture. Controls preferred a “character viewpoint” or first-person perspective where their hands represented the hands of the actor, whereas PD patients preferred an “observer viewpoint” or third-person perspective, where their hand represented a whole person. This finding was replicated and extended in Chapter 3 where low-motion and high-motion actions were described in a longer narrative task. PD patients produced fewer character viewpoint gestures when describing high-motion action events, suggesting a difficulty in simulating these events from a first-person perspective. In addition, PD patients had difficult depicting “manner” (how an action is performed) features in gesture during high but not low motion. Extending the findings of Chapter 2, whilst overall rate of gesture production was not affected, PD patients produced action gestures at a significantly lower rate than controls. Chapter 4 took a different focus by investigating gesture depictions of static spatial (rather than dynamic action) features via a house description task. Gesture rate did not differ, but the groups depicted different types of spatial properties to a different extent. Whilst both groups predominantly gestured about location and relative position information, PD patients gestured more about directions whereas controls gestures more about shape and size information. This suggests that different strategies were being employed by the two groups. Finally, testing young adults’ comprehension of these spatial gestures in Chapter 5 revealed that gestures did not significantly improve comprehension of either PD patients’ or controls’ spoken messages, though there may have been ceiling effects. However, both PD patients and controls were viewed as more competent when their messages were viewed with gestures. The findings suggest a selective action-gesture deficit in PD which complements work demonstrating action-verb impairments in these patients, and supports gesture production theories which hypothesise a role for motor simulations and imagery. Overall gesture rate appears to be largely unaffected. The effects of PD can be felt beyond changes to goal-directed action, in the realms of language and social behaviour, but gestures may be able to improve listeners’ social perceptions of PD patients.
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5رسالة جامعية
المؤلفون: Nagarajan, Aarthy
المساهمون: Guan Cuntai, School of Computer Science and Engineering, Centre for Brain-Computing Research (CBCR), CTGuan@ntu.edu.sg
مصطلحات موضوعية: Computer and Information Science, Brain-computer interfaces, Motor imagery
الوصف: Brain-computer interfaces (BCIs) provide a means of non-muscular communication by translating brain activity into the control of external devices. Motor imagery (MI) has attracted significant attention among various non-invasive BCI paradigms using electroencephalogram (EEG) for its potential in stroke rehabilitation. However, MI-based BCIs encounter challenges in real-time applications for stroke patients, primarily due to limited reliability and robustness. Additionally, the scarce availability of clinical data impedes the development of cross-subject models for MI detection in stroke patients. Furthermore, the current MI-BCIs do not adequately facilitate the restoration of distal hand functions, which are essential for enhancing the quality of life for individuals with motor impairments. This thesis proposes solutions to address these technical challenges in BCIs for stroke rehabilitation using deep learning (DL) methods. Furthermore, a novel experimental protocol is introduced to enable clinically relevant practical applications of BCIs in stroke patients. The research begins with an extensive literature review focusing on the impact of EEG discrepancies on the performance of BCIs. The review delves into channel selection and transfer learning techniques that aim to enhance the resilience of EEG-BCIs. Recently, there has been a surge in studies investigating subject-independent models in the domain of MI-BCI. This trend is driven by the superior predictive capabilities of subject-independent models based on DL compared to subject-specific models. However, the literature review highlights a significant gap in the research, as most studies in this area have focused primarily on healthy subjects, with limited inclusion of stroke patients. Furthermore, the review encompasses relevant studies exploring MI decoding from the same limb. With the goal of selecting the optimal set of EEG channels to enhance overall classification performance in DL-based MI-BCIs, the author proposes subject-independent channel ...
وصف الملف: application/pdf
العلاقة: A20G8b0102; Nagarajan, A. (2024). Mitigating technical challenges in brain-computer interfaces for stroke rehabilitation. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/174103Test; https://hdl.handle.net/10356/174103Test
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6رسالة جامعية
المؤلفون: 中島, 輝
المصدر: Neural Regeneration Research. 16 6
مصطلحات موضوعية: central nervous system, concentration, continuous repetition of motor imagery, corticospinal excitability, mental fatigue, motor evoked potential, motor imagery, muscle fatigue, neurophysiology, transcranial magnetic stimulation
وصف الملف: PDF
Degree: 博士(医学) -- Nagasaki University (長崎大学)
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7رسالة جامعية
المؤلفون: Castellanos Delgado, Johan Sebastian
المساهمون: Gallo Sanchez, Luisa Fernanda, Gonzalez Morales, David Fernando, La Cruz Punte, Alexandra
مصطلحات موضوعية: Señales electroencefalográficas, Deep learning, Motor imagery, Electroencephalogram, Motor imagery classification, Brain-computer interface, Aprendizaje profundo, Imágenes motoras, Electroencefalograma, clasificación de imágenes motoras, Interfaz Cerebro-Computadora
الوصف: Documento con 116 paginas donde esta incluido todo el trabajo de investigacion, los anexos, diferentes ilustaciones, imagenes, datos y resultados. ; El sistema de interfaz Cerebro-Computadora (BCI) proporciona un canal entre el cerebro y un dispositivo electrónico, para que estos dispositivos sean controlados con la actividad eléctrica del cerebro sin tener que usar el sistema nervioso periférico. Los BCI se utilizan en aplicaciones médicas, por ejemplo, para controlar prototipos neuroprotésico, estos se pueden controlar con diferentes señales de electroencefalografía (EEG), entre las más utilizadas están las ondas P300 y potenciales evocados de estado estable (SSVEP). Las imágenes motoras (MI) son otra variante de señales EEG para la implementación en un sistema BCI, han ganado mucha atención recientemente ya que estas señales codifican la intención de una persona de realizar una acción. En este trabajo, se desarrolló una base de datos con señales IM de 2 personas completamente sanas, adquiridas con la diadema comercial EMOTIV EPOC+ y el software OpenVibe, las 2 personas tenían que imaginar mover la mano izquierda o derecha. Se aplica un preprocesamiento de las señales con la librería MNE en Python y un clasificador con una red neuronal convolucional (CNN), implementando la arquitectura EEGNet desarrollada en TensorFlow para la clasificación de IM de la mano izquierda y derecha. Se realizaron varios experimentos para evaluar el método propuesto, obteniendo un clasificador del 70% para el sujeto 1 y del 77% para el sujeto 2. Para controlar la simulación de prótesis, se cargó el mejor modelo entrenado y se controló con una comunicación serial entre Python y Arduino, para poder tener una representación de simulación de una BCI. ; The brain-computer interface (BCI) system provides a channel between the brain and an electronic device, so that these devices can be controlled with the electrical activity of the brain without having to use the peripheral nervous system. BCIs are used in medical applications, for ...
وصف الملف: 116 páginas; application/pdf
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8رسالة جامعية
المؤلفون: Chagnon, Laura
المساهمون: Aix-Marseille Université - Faculté des sciences médicales et paramédicales (AMU SMPM), Aix Marseille Université (AMU), Stephan Rostagno
المصدر: https://dumas.ccsd.cnrs.fr/dumas-04239375Test ; Sciences du Vivant [q-bio]. 2023.
مصطلحات موضوعية: Central neuropathic pain, Motor imagery, Graduated motor imagery, Mirror therapy-mental imagery, Pain intensity, Douleurs neuropathiques centrales, Imagerie motrice, Imagerie motrice graduelle, Thérapie miroir, Imagerie mental, Intensité de la douleur, [SDV]Life Sciences [q-bio], [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
الوصف: Background: Central neuropathic pain is a chronic pain defined as unpleasant, painful or even unbearable sensations that can occur after the affection of the somatosentorial system. To date, many treatments exist to reduce this pain (pharmacological, surgical but also non-pharmacological) including motor imagery. This treatment includes three types of therapies: graded motor imagery, mental imagery and mirror therapy. Objectives: This literature review aims to determine the effects of motor imagery in the management of patients with central neuropathic pain. Methods: Searches were conducted on several databases such as Medline via PubMed, PEDro, Cochrane, LiSSa, Kinédoc based on a precise search equation developed beforehand. Subsequently, a rigorous selection of studies to be included was made. This selection was based on predefined inclusion and exclusion criterias in order to obtain the most relevant studies to answer the clinical question. Results: Five randomised clinical trials were included and analysed in this review. Four studies showed statistically significant results in favour of the use of motor imagery to reduce central neuropathic pain. Also four of these studies show clinical relevance. However, the statistical power of the results obtained is diminished due to the large size of the confidence intervals. Conclusion: The small sample sizes, the numerous heterogeneities in these studies and the biases highlighted do not allow us to conclude with certainty on the effectiveness of motor imagery in the management of central neuropathic pain. However, the results obtained tend to show a positive effect of this therapy. More precise and reliable studies are needed to confirm the positive effect or not of motor imagery ; Introduction : les douleurs neuropathiques de types centrales sont des douleurs chroniques définies comme des sensations désagréables, pénibles voire insupportables qui peuvent survenir après l’atteinte du système somatosentoriel. A ce jour, beaucoup de traitements existent afin de ...
العلاقة: dumas-04239375; https://dumas.ccsd.cnrs.fr/dumas-04239375Test; https://dumas.ccsd.cnrs.fr/dumas-04239375/documentTest; https://dumas.ccsd.cnrs.fr/dumas-04239375/file/CHAGNON%20Laura_11363568_assignsubmission_file_Revue%20de%20litt%C3%A9rature%20CHAGNON%20Laura.pdfTest
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9رسالة جامعية
المؤلفون: Mahmudi, Osama, Mishra, Shubhra
مصطلحات موضوعية: Adversarial Learning, Motor Imagery, BCI, EEG, Machine Learning, Information Systems, Systemvetenskap, informationssystem och informatik
الوصف: Context: Motor Imagery (MI) signal classification is a crucial task for developing Brain-Computer Interfaces (BCIs) that allow people to control devices using their thoughts. However, traditional machine learning approaches often suffer from limited performance due to inter-subject variability and limited data availability. In response, adversarial learning has emerged as a promising solution to enhance the resilience and accuracy of BCI systems. However, to the best of our knowledge, there has not been a review of the literature on adversarial learning specifically focusing on MI classification. Objective: The objective of this thesis is to perform a Systematic Literature Review (SLR) focusing on the latest techniques of adversarial learning used to classify motor imagery signals. It aims to analyze the publication trends of the reviewed studies, investigate their use-cases, and identify the challenges in the field. Additionally, this research recognizes the datasets used in previous studies and their associated use-cases. It also identifies the pre-processing and adversarial learning techniques, and compare their performance. Additionally, it could aid in evaluating the replicability of the studies included. The outcomes of this study will assist future researchers in selecting appropriate datasets, pre-processing, and adversarial learning techniques to advance their research objectives. The comparison of models will also provide practical insights, enabling researchers to make informed decisions when designing models for motor imagery classification. Furthermore, assessing reproducibility might help in validating the research outcomes and hence elevate the overall quality of future research. Method: A thorough and systematic search following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines is undertaken to gather primary research articles from several databases such as Scopus, Web of Science, IEEEXplore, PubMed, and ScienceDirect. Two independent reviewers evaluated the articles obtained based on predetermined eligibility criteria at the title-abstract level, and their agreement was measured using Cohen's Kappa. The articles that fulfill the criteria are then scrutinized at the full-text level by the same reviewers. Any discrepancies are resolved by the judge – played by the supervisor. Critical appraisal was employed to choose appropriate studies for data extraction, which was subsequently examined using bibliometric and descriptive analyses to answer the research questions. Result: The study's findings indicate substantial growth within the domain over the past six years, notably propelled by contributions from the Asian region. However, the need for augmented collaboration becomes evident as evidenced by the prevalence of insular co-author networks. Four principal use-cases for adversarial learning are identified, spanning data augmentation, domain adaptation, feature extraction, and artifact removal. The favored datasets are BCI Competition IV's 2a and 2b, often accompanied by band-pass filtering and exponential moving standardization preprocessing. This study identifies two primary adversarial learning techniques: GAN and Adversarial Training. GAN is mainly used for data augmentation and artifact removal, while adversarial training is employed for domain adaptation and feature extraction. Based on the results reported in the chosen papers, the accuracy achieved for data augmentation and domain adaptation use cases is nearly identical at 95.3%, while the highest accuracy for the feature extraction use case is 86.91%. However, for artifact removal, both correlation and root mean square methods have been referenced. Furthermore, a reproducibility table has been established which may help in evaluating the replicability of the selected studies. Conclusion: The outcomes provide researchers with valuable perspectives on less-explored areas that hold room for additional enhancement. Ultimately, these perspectives hold the promise of improving the practical applications intended to support individuals dealing with motor impairments.
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10رسالة جامعية
المؤلفون: Högdal, Anna
مصطلحات موضوعية: mental simulering, motor imagery, rehabilitering, subacromiellt smärtsyndrom, SAPS, rotatorcuff, Physiotherapy, Sjukgymnastik, Sport and Fitness Sciences, Idrottsvetenskap
الوصف: Bakgrund: Subacromiellt smärtsyndrom och rotatorcuffrelaterade besvär behandlas vanligtvis med rehabilitering i from av fysisk träning. Forskning visar mer och mer på positiv effekt av motorsimulering (MI) vid både träning och rehabilitering i from av bland annat ökad styrka och rörlighet. Området är dock inte tillräckligt utforskat och det saknas forskning på just SAPS och rotatorcuffproblematik.Syfte: Syftet med studien var att undersöka om ett tillägg av mental träning genom MI vid rehabilitering av SAPS eller rotatorcuffrelaterade besvär kan bidra till ytterligare effekt i form av minskad smärta och förbättrad upplevd funktionsnivå.Metod: Studien utfördes som en experimentell studie med interventionsgrupp (n=10) som utförde fysisk rehabilitering och MI samt referensgrupp (n=11) som utförde fysisk rehabilitering under 8 veckor. Upplevd funktion och smärta undersöktes med WORC och NRS vid rehabiliteringsperiodens start, efter 4 veckor och efter 8 veckor. Data analyserades med Mann-Whitney U test och ANOVA repeated measures. WORC analyserades i sin helhet samt dess delområden och smärtan utifrån NRS och WORC:s två frågor om smärtaResultat: Det noterades inga signifikanta skillnader mellan grupperna vid någon tidpunkt. För interventionsgruppen förelåg en signifikant förbättring på WORC:s totala poäng samt delområdena Fysisk, Arbete och Livsstil samt för smärtskattning VAS skarp smärta. För referensgruppen förelåg en signifikant förbättring för WORC:s delområden Fysiska, Arbete och Känslor samt NRS och smärtskattning VAS skarp smärta.Slutsats: Resultatet visar tendenser till att MI kan ha effekt som komplement till fysisk rehabilitering vid SAPS eller rotatorcuffsrelaterade besvär vad gäller upplevd funktion men inte för smärtminskning. Detta innebär att MI skulle kunna vara ett alternativ vid behandling av dessa besvär. Denna studie genomfördes som en pilotstudie med ett relativt lågt deltagarantal vilket gör att resultaten bör tolkas med försiktighet.
وصف الملف: application/pdf
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