Feature Extraction for fMRI-Based Human Brain Activity Recognition

2010 ◽  
pp. 148-156
Author(s):  
Wei Bian ◽  
Jun Li ◽  
Dacheng Tao
Keyword(s):  
Feature Extraction ◽  
Human Brain ◽  
Activity Recognition ◽  
Brain Activity

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

Human brain activity during memory

Science ◽  
2020 ◽  
Vol 367 (6482) ◽  
pp. 1086.8-1087
Author(s):  
Peter Stern
Keyword(s):  
Human Brain ◽  
Brain Activity

A hybrid WD-EEMD sEMG feature extraction technique for lower limb activity recognition

2021 ◽  
pp. 1-1
Author(s):  
Ankit Vijayvargiya ◽  
Vishu Gupta ◽  
Rajesh Kumar ◽  
Nilanjan Dey ◽  
Joao Manuel R. S. Tavares
Keyword(s):  
Feature Extraction ◽  
Activity Recognition ◽  
Lower Limb ◽  
Extraction Technique ◽  
Feature Extraction Technique

Mathematical dipoles are adequate to describe realistic generators of human brain activity

1988 ◽  
Vol 35 (11) ◽  
pp. 960-966 ◽  
Author(s):  
J.C. de Munck ◽  
B.W. van Dijk ◽  
H. Spekreijse
Keyword(s):  
Human Brain ◽  
Brain Activity

scholarly journals Time-Invariant Person-Specific Frequency Templates in Human Brain Activity

2006 ◽  
Vol 96 (25) ◽  
Author(s):  
Itai Doron ◽  
Eyal Hulata ◽  
Itay Baruchi ◽  
Vernon L. Towle ◽  
Eshel Ben-Jacob
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Specific Frequency ◽  
Time Invariant
Sign in / Sign up

Export Citation Format

Share Document