Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Improved Motor Imagery Decoding Using Deep Learning Techniques, Olawunmi Olaboopo George
Improved Motor Imagery Decoding Using Deep Learning Techniques, Olawunmi Olaboopo George
Dissertations (1934 -)
Motor imagery (MI) has been one of the most used paradigms for building brain-computer interfaces (BCI), widely used in neurorehabilitation, for restoring functionality to damaged parts of a neurologically deficient person. The existing motor imagery techniques have largely employed feature extraction techniques such as the power spectral density (PSD) and the common spatial patterns (CSP) before classification, using traditional machine learning algorithms such as support vector machines (SVM) and linear discriminant analysis (LDA). These algorithms are quite limited in their ability to generate feature representations for certain types of signals, limiting the potential for improvements in the decoding process. Also, …
Feature Space Augmentation: Improving Prediction Accuracy Of Classical Problems In Cognitive Science And Computer Vison, Piyush Saxena
Feature Space Augmentation: Improving Prediction Accuracy Of Classical Problems In Cognitive Science And Computer Vison, Piyush Saxena
Dissertations (1934 -)
The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as …
Designing Human-Centered Collective Intelligence, Ivor Addo
Designing Human-Centered Collective Intelligence, Ivor Addo
Dissertations (1934 -)
Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting …