9 citations · 58 across the 23 of their papers we have counts for
4 papers · 1 filter
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Peizhen Yang, Xinke Shen, Zongsheng Li +3
Identifying causal relationships among distinct brain areas, known as effective connectivity, holds key insights into the brain's information processing and cognitive functions. El…
A novel convolutional neural network model to remove muscle artifacts from EEG
Haoming Zhang, Chen Wei, Mingqi Zhao +2
The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts. In recent years, deep learning models have been used for denoising of electroencephalo…
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
Haoming Zhang, Mingqi Zhao, Chen Wei +3
Deep learning networks are increasingly attracting attention in various fields, including electroencephalography (EEG) signal processing. These models provided comparable performan…
Fitts' Law for speed-accuracy trade-off describes a diversity-enabled sweet spot in sensorimotor control
Yorie Nakahira, Quanying Liu, Terrence J. Sejnowski +1
Human sensorimotor control exhibits remarkable speed and accuracy, and the tradeoff between them is encapsulated in Fitts' Law for reaching and pointing. While Fitts related this t…