Object categorization using single-trial electroencephalography (EEG) data measured while
participants view images has been studied intensively. In previous studies, multiple event …

Recently, advances in noninvasive detection techniques have shown that it is possible to
decode visual information from measurable brain activities. However, these studies typically …

With the development of deep learning in medical image analysis, decoding brain states
from functional magnetic resonance imaging (fMRI) signals has made significant progress …

Decoding perceptual experience from human brain activity is a big challenge in
neuroscience. Recent advances in human neuroimaging have shown that it is possible to …

Purpose The aim of this study is to compare the blood oxygen level–dependent (BOLD)
fluctuation power in 96 frequency points ranging from 0 to 0.25 Hz between benign and …

Decoding of brain tasks aims to identify individuals' brain states and brain fingerprints to
predict behavior. Deep learning provides an important platform for analyzing brain signals at …

利用鸽视顶盖神经元对视觉图像刺激产生的局部场电位信号(LFP) ꎬ 重建刺激数字字符图像ꎮ
采用微电极阵列记录数字图像扫屏刺激下的神经元LFP 信号ꎬ 对其进行傅里叶变换并提取幅值 …

Visual encoding and decoding is an important tool to explore brain neural function, how to
establish an efficient and accurate encoding model is an important issue in fMRI brain visual …

The structure of human brain is complex, and fMRI data can be used to reveal the working
mechanism of human brain. We construct a generative confrontation deep learning network …

Present feature selection algorithm is very complex and not accurate enough. So this paper
uses SFFS (Sequential Floating Forward Selection) algorithm to implement feature selection …