1,720,958 research outputs found
EEG-Based Multi-Class Classification for Recognizing Pedaling Velocities: A Promising Approach for Brain-Computer Interface-Enhanced Lower-Limb Robotic Rehabilitation
No full textLower-limb robotic rehabilitation devices have shown promising results in restoring functional activities of individuals with motor impairments. Motorized Mini-Exercise Bikes (MMEBs) are robotic devices that provide functional and repetitive flexion/extension exercises through Passive Pedaling (PP), improving muscular activity, coordination, and functional independence. These technologies activated by Brain-Computer Interfaces (BCIs) can be useful during therapeutic interventions for individuals who cannot voluntarily perform a movement. This study proposes a multi-class classification strategy for recognizing different pedaling velocities using electroencephalography signals. To provide a meaningful task, we conducted a protocol with ten able-bodied subjects who received PP from an MMEB configured at 30, 45, and 60 rpm. We provide a practical demonstration by implementing a feature extraction stage based on Common Spatial Patterns, Power Spectral Density, and a classification system based on four Machine Learning techniques: Linear Discriminant Analysis, Support Vector Machine, k-nearest Neighbors, and Decision Tree. Our strategy achieved a mean accuracy of 0.86, false positive rate of 0.13, and kappa index of 0.64, we concluded that the system has potential to be used in the design of robust controllers for robotic devices. Further research will be centered on real-time evaluation of our strategy to implement an MMEB-based BCI for personalized rehabilitation of people with lower-limb limitations, such as stroke or spinal cord injury survivors
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Methodology based on machine learning through neck motion and POF-based pressure sensors for wheelchair operation
No full textPolymer Optical Fiber (POF)-based sensors have gained recognition in recent years for biomedical applications because of their low cost, physical properties, and feasibility. A novel methodology is proposed here for classifying neck movements using POF- based pressure sensors and machine learning algorithms. To address this, signal pre-processing, feature extraction, and selection methods are implemented, considering variance, root mean square, and Hjorth parameters. Linear Discriminant Analysis, Support Vector Machine, k-Nearest Neighbors (kNN), and Decision Tree (DT) were used for classification. A maximum accuracy of 0.91 was obtained with kNN and DT for recognizing four neck movements by using the best discriminant nine features. These findings indicate that the proposed methodology is suitable for neck-motion classification using POF-based pressure sensors. Future work will focus on the implementation of this strategy for the design of intelligent Human Machine Interfaces based on electric-powered wheelchairs, which would allow for more independence for people with upper- and lower-limb disabilities
Enhancing complex upper-limb motor imagery discrimination through an incremental training strategy
No full textMotor Imagery (MI)-based Brain–Computer Interface (BCI) systems are a great technological advance for the recovery of lost movements in people with severe motor impairments. Different Artificial Intelligence (AI) techniques with supervised methods have been explored for MI task discrimination, especially static movements from left and right hands. Due to different factors affecting MI-based Electroencephalography (EEG) signals related to physical and cognitive conditions, the success rate of BCIs is still low. Currently, there is a need to explore the MI of complex movements associated with Activities of Daily Living (ADLs), which brings challenges to the scientific community. In this work, an incremental training methodology for Artificial Neural Networks (ANNs) is proposed for discrimination of complex MI tasks. MI-Rest and MI-Task related to the imagination of manipulating a drinking cup were discriminated by implementing an Action Observation (AO)-based protocol in a first-person 2D virtual reality. Thirty healthy individuals were recruited to evaluate our complex MI classification approach. The incremental training proposed achieves significantly higher performance () compared to the non-incremental training. Additionally, the proposed method is significantly superior compared to widely applied methods for MI task discrimination: Power Spectrum (PS), Common Spatial Patterns (CSP), Filter Bank Common Spatial Patterns (FBCSP), each using four baseline Machine Learning (ML) methods for classification. The results show an improvement between 5 and 20% according to both Accuracy (ACC) and False Positive Rate (FPR). The proposed methodology obtained promising results, useful for AI-based BCI training, which would allow the development of more robust systems for neurorehabilitation purposes
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