Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it into recurring entities, we propose ...
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Particle Swarm Optimization (PSO) is a swarm intelligence meta-heuristics whose performance highly depends on the selection of its hyper-parameters, which control the particles’ exploration and exploitation...
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HCI professionals have developed design principles, guidelines, and standards dealing with consistency, informative feedback, error prevention, shortcuts for experts, and user control. Micro-HCI researchers can take c...
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HCI professionals have developed design principles, guidelines, and standards dealing with consistency, informative feedback, error prevention, shortcuts for experts, and user control. Micro-HCI researchers can take comfort in dealing with wellstated requirements, clear benchmark tasks, established measures of human performance, and effective predictive models, such as Fitts' Law. Macro-HCI researchers and developers design and build interfaces in expanding areas, such as affective experience, aesthetics, motivation, social participation, trust, empathy, responsibility, and privacy. Although micro-HCI and macro-HCI have healthy overlaps, they attract different types of researchers, practitioners, and activists, thereby further broadening the scope and impact of HCI in general. Since commercial, social, legal, and ethical considerations play an increasing role in all areas of HCI, educational curricula and professional practices need to be updated regularly;midcareer continuing education for HCI professionals will help keep them current.
In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and events surrounding certain photographs. However, there are few dedicated tool...
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One of the limitations of most virtual reality (VR) systems is that users cannot physically walk through large virtual environments. Many solutions have been proposed to this problem, including locomotion devices such...
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We present a novel virtual musical instrument interface based on in-air 3D gestures called the Interval Player. Instead of specifying absolute notes, the user specifies the melodic interval between the most recent not...
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We designed a collaborative navigation interface in which one user (the controller) guides a second, remote user (the navigator) through a 3D game environment. Both sides of the interface use commercially available ga...
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More modes are. better than one when it corner fa comprehending human speech, especially when, speakers are accented or interacting innoisy natural environments.A MULTIMODAL ARCHITECTURE CAN FUNCTION MORE ROBUSTLY THA...
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More modes are. better than one when it corner fa comprehending human speech, especially when, speakers are accented or interacting innoisy natural environments.A MULTIMODAL ARCHITECTURE CAN FUNCTION MORE ROBUSTLY THAN ANYindividual recognition technology that is inherently error prone, includingspoken-language systems. One goal of a well-designed muhimodal system is the integration of complementary input modes to create a synergistic blend, permitting the strengths of each mode to overcome weaknesses in the other modes and to support "mutual compensation" of recognition errors.
Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its su...
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Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-computer interface (BCI) applications, especially for individuals with physical disabilities. However, accurate classification of MI EEG signals remains a major challenge due to their non-stationary nature, low signal-to-noise ratio, and sensitivity to both external and physiological noise. Traditional classification methods, such as common spatial pattern (CSP), often assume that the data is stationary and Gaussian, which limits their applicability in real-world scenarios where these assumptions do not hold. These challenges highlight the need for more robust methods to improve classification accuracy in MI-BCI systems. To address these issues, this study introduces a Riemannian geometry-based spatial filtering (RSF) method that projects EEG signals into a lower-dimensional subspace, maximizing the Riemannian distance between covariance matrices from different classes. By leveraging the inherent geometric properties of EEG data, RSF enhances the discriminative power of the features while maintaining robustness against noise. The performance of RSF was evaluated in combination with ten commonly used MI decoding algorithms, including CSP with linear discriminant analysis (CSP-LDA), Filter Bank CSP (FBCSP), Minimum Distance to Riemannian Mean (MDM), Tangent Space Mapping (TSM), EEGNet, ShallowConvNet (sCNN), DeepConvNet (dCNN), FBCNet, Graph-CSPNet, and LMDA-Net, using six publicly available MI-BCI datasets. The results demonstrate that RSF significantly improves classification accuracy and reduces computational time, particularly for deep learning models with high computational complexity. These findings underscore the potential of RSF as an effective spatial filtering approach for MI EEG classification, providing new insight
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