AIMC Topic: Attention

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TFDISNet: Temporal-frequency domain-invariant and domain-specific feature learning network for enhanced auditory attention decoding from EEG signals.

Biomedical physics & engineering express
Auditory Attention Decoding (AAD) from Electroencephalogram (EEG) signals presents a significant challenge in brain-computer interface (BCI) research due to the intricate nature of neural patterns. Existing approaches often fail to effectively integr...

Dual Attention-Based recurrent neural network and Two-Tier optimization algorithm for human activity recognition in individuals with disabilities.

Scientific reports
Human activity recognition (HAR) has been one of the active research areas for the past two years for its vast applications in several fields like remote monitoring, gaming, health, security and surveillance, and human-computer interaction. Activity ...

Collective motion model inspired by fish school based on deep attention mechanism.

Bioinspiration & biomimetics
Collective intelligence in biological groups can be employed to inspire the control of artificial complex systems, such as swarm robotics. However, modeling for the social interactions between individuals is still a challenging task. Without loss of ...

Cognitive impairment assessment using eye-tracking: multilevel saccade paradigms with differential analysis and attention-based neural networks.

Physiological measurement
. The accurate assessment of cognitive impairment plays a vital role in more targeted treatments for Dementia. Eye movement analysis is a non-invasive and objective method that offers fine-grained insight into cognitive functioning, complementing con...

Predictive robot eyes enhance attentional guidance in cooperative human-robot interaction.

Scientific reports
A key factor in successful human-robot interaction (HRI) is the predictability of a robot's actions. Visual cues, such as eyes or arrows, can serve as directional indicators to enhance predictability, potentially improving performance and increasing ...

A Compound-Eye-Inspired Multi-Scale Neural Architecture with Integrated Attention Mechanisms.

International journal of neural systems
In the context of neural system structure modeling and complex visual tasks, the effective integration of multi-scale features and contextual information is critical for enhancing model performance. This paper proposes a biologically inspired hybrid ...

How musicality enhances top-down and bottom-up selective attention: Insights from precise separation of simultaneous neural responses.

Science advances
Natural environments typically contain a blend of simultaneous sounds. A substantial challenge in neuroscience is identifying specific neural signals corresponding to each sound and analyzing them separately. Combining frequency tagging and machine l...

Enhanced epileptic seizure detection using CNNs with convolutional block attention and short-term memory networks.

Behavioural brain research
Analyzing the electroencephalography (EEG) signals of epilepsy patients can monitor the condition, detect and intervene in epileptic seizures in time. To enhance the lives of these patients, it is necessary to develop accurate methods to detect epile...

AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.

Neuroscience
Early and accurate Alzheimer's disease (AD) diagnosis is critical for effective intervention, but it is still challenging due to neurodegeneration's slow and complex progression. Recent studies in brain imaging analysis have highlighted the crucial r...

Hierarchical query design and distributed attention in transformer for player group activity recognition in sports analysis.

Scientific reports
Group activity recognition in sports analysis is a critical challenge in computer vision, requiring robust modeling of complex player interactions and dynamic scenarios. Existing approaches predominantly rely on region-based features and two-stage pi...