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Temporal-spatial cross attention network for recognizing imagined characters.

Scientific reports
Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features ...

AutoAMS: Automated attention-based multi-modal graph learning architecture search.

Neural networks : the official journal of the International Neural Network Society
Multi-modal attention mechanisms have been successfully used in multi-modal graph learning for various tasks. However, existing attention-based multi-modal graph learning (AMGL) architectures heavily rely on manual design, requiring huge effort and e...

Attention Analysis in Robotic-Assistive Therapy for Children With Autism.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Children with Autism Spectrum Disorder (ASD) show severe attention deficits, hindering their capacity to acquire new skills. The automatic assessment of their attention response would provide the therapists with an important biomarker to better quant...

Omnidirectional image super-resolution via position attention network.

Neural networks : the official journal of the International Neural Network Society
For convenient transmission, omnidirectional images (ODIs) usually follow the equirectangular projection (ERP) format and are low-resolution. To provide better immersive experience, omnidirectional image super resolution (ODISR) is essential. However...

GRAM: An interpretable approach for graph anomaly detection using gradient attention maps.

Neural networks : the official journal of the International Neural Network Society
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often lack interpretability, which hinders our understanding of anomaly detect...

The attentive reconstruction of objects facilitates robust object recognition.

PLoS computational biology
Humans are extremely robust in our ability to perceive and recognize objects-we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward p...

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection chal...

Classification of short and long term mild traumatic brain injury using computerized eye tracking.

Scientific reports
Accurate, and objective diagnosis of brain injury remains challenging. This study evaluated useability and reliability of computerized eye-tracker assessments (CEAs) designed to assess oculomotor function, visual attention/processing, and selective a...

Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.

IEEE transactions on neural networks and learning systems
The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretc...

Spiking generative adversarial network with attention scoring decoding.

Neural networks : the official journal of the International Neural Network Society
Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural n...