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An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when...

A machine learning approach to understanding the road and traffic environments of crashes involving driver distraction and inattention (DDI) on rural multilane highways.

Journal of safety research
INTRODUCTION: Driver distraction and inattention (DDI) are major causes of road crashes, especially on rural highways. However, not all instances of distracted or inattentive driving lead to crashes. Previous studies indicate that DDI-related driving...

Object Recognition Using Shape and Texture Tactile Information: A Fusion Network Based on Data Augmentation and Attention Mechanism.

IEEE transactions on haptics
Currently, most tactile-based object recognition algorithms focus on single shape or texture recognition. However, these single attribute-based recognition methods perform poorly when dealing with objects with similar shape or texture characteristics...

Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models.

Scientific reports
Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we...

Frequency-Assisted Local Attention in Lower Layers of Visual Transformers.

International journal of neural systems
Since vision transformers excel at establishing global relationships between features, they play an important role in current vision tasks. However, the global attention mechanism restricts the capture of local features, making convolutional assistan...

A novel deep learning model combining 3DCNN-CapsNet and hierarchical attention mechanism for EEG emotion recognition.

Neural networks : the official journal of the International Neural Network Society
Emotion recognition plays a key role in the field of human-computer interaction. Classifying and predicting human emotions using electroencephalogram (EEG) signals has consistently been a challenging research area. Recently, with the increasing appli...

[Neural network for auditory speech enhancement featuring feedback-driven attention and lateral inhibition].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can ...

Attention-augmented multi-domain cooperative graph representation learning for molecular interaction prediction.

Neural networks : the official journal of the International Neural Network Society
Accurate identification of molecular interactions is crucial for biological network analysis, which can provide valuable insights into fundamental regulatory mechanisms. Despite considerable progress driven by computational advancements, existing met...

Classifying metro drivers' cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination.

Scientific reports
Metro drivers are more likely to trigger accidents if they suffer from cognitive distractions during manual driving. However, identifying metro drivers' cognitive distractions faces challenges as generally no obvious behavior can be found during the ...

HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection.

IEEE transactions on cybernetics
Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection...