Adaptive frequency band attention-guided CNN-BiLSTM for Spatial-Spectral-Temporal EEG emotion recognition [0.03%]
自适应频段注意引导的CNN-BiLSTM脑电空间-谱-时情绪识别方法
Rabita Hasan,Sheikh Md Rabiul Islam
Rabita Hasan
Electroencephalography (EEG)-based emotion recognition has gained increasing attention in affective computing because EEG provides high temporal resolution and reflects intrinsic neural activity. However, reliable emotion recognition remain...
Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection [0.03%]
高效且数据高效的视觉Transformer-CNN统一框架用于可解释的癫痫发作检测
Aaranay Aadi,Divyansh Sukhija,Rishabh Shetty et al.
Aaranay Aadi et al.
Epileptic seizures are short episodes of abnormal electrical activity in the brain that can cause convulsions, loss of consciousness, and other similar symptoms. Despite therapy, around 30% of patients with epilepsy continue to have seizure...
Decoding neuronal gene expression: integrative insights from omics and AI [0.03%]
利用组学和人工智能综合解析神经元基因表达
Aranyak Goswami,Rushikesh R Lagad,Shakil Rafi
Aranyak Goswami
Neuronal functional diversity and pathological vulnerability are governed by multi-layered regulatory programs. While high-throughput omics and neuroimaging provide high-resolution snapshots of these programs, bridging the gap between molec...
Hybrid self-supervised EEG emotion representation: masked reconstruction joint with mutual information bounds [0.03%]
基于掩码重建和互信息界的 hybrid 自监督脑电情绪表示方法
Haoyu Liu,Xinyu Li,Haiyan Zhou et al.
Haoyu Liu et al.
EEG signals are widely used in affective computing and brain informatics for emotion recognition due to their non-invasiveness. Deep learning and self-supervised learning (SSL) are key for EEG representation learning: masked reconstruction ...
EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography [0.03%]
EEG-DBNET：一种用于运动想象脑电图时频表征学习的双分支框架
Youxi Qu,Xicheng Lou,Hongying Meng et al.
Youxi Qu et al.
Purpose: Motor imagery electroencephalography (MI-EEG) decoding remains challenging due to low signal-to-noise ratio and complex temporal-spectral characteristics. This study aims to develop a robust deep learning framewo...
Cross-attention-guided subject-adaptive graph learning for multimodal autism classification: integrating structural and functional MRI data [0.03%]
跨注意力引导的主题自适应图学习的多模态自闭症分类：融合结构磁共振和功能磁共振数据
Yan Tang,Chao Yang,Yihang Xu et al.
Yan Tang et al.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by structural atypicality and abnormal functional connectivity. It remains challenging to accurately delineate an ASD-associated neural marker due to individual...
Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction [0.03%]
通用性和可解释性的深度学习在脑MRI中的应用：评估三维架构在年龄和性别预测中的多队列表现
Radhika Juglan,Marta Ligero,Zunamys I Carrero et al.
Radhika Juglan et al.
Deep learning (DL) methods increasingly outperform classical approaches in brain MRI analysis, yet their generalizability across independent imaging cohorts remains insufficiently evaluated. Because age and sex are fundamental neurobiologic...
Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable sub-band topography [0.03%]
基于优化注意力的深度学习 Parkinson病分类算法及其具有可解释性的子频带拓扑图
Khosro Rezaee,Hossein Ghayoumi Zadeh,Ali Fayazi
Khosro Rezaee
Parkinson's disease (PD) diagnosis remains challenging because subtle neural alterations may be difficult to capture using conventional clinical assessment alone. This study proposes an attention-based deep learning framework for classifyin...
A quantitative and precision‑oriented neuronal reconstruction approach based on data grading [0.03%]
基于数据分级的定量和精确导向型神经元重构方法
Mingwei Liao,Chi Xiao,Xiaojun Wang et al.
Mingwei Liao et al.
Accurate and efficient neuronal reconstruction is essential for large-scale neuronal projection analysis and neural circuit mapping. However, conventional reconstruction approaches are often constrained by the structural complexity of neuro...
Evaluating multi-level membership inference risk in federated EEG learning [0.03%]
联邦EEG学习中多级成员推理风险的评估
Taslima Khanam,Siuly Siuly,Kate Wang et al.
Taslima Khanam et al.
Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communication, and assistive technologies. EEG is widely used in motor-imagery (MI) based BCIs, w...