scholarly journals Prediction of Human Brain Activity Using Likelihood Ratio Based Score Fusion

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 13010-13019 ◽  
Author(s):  
Raheel Zafar ◽  
Sarat C. Dass ◽  
Aamir Saeed Malik ◽  
Nidal Kamel ◽  
M. Javvad Ur Rehman ◽  
...  
Keyword(s):  
Human Brain ◽  
Likelihood Ratio ◽  
Brain Activity ◽  
Score Fusion

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

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

Theory of the Relation between Human Brain Activity (MEG) and Hand Movements

NeuroImage ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Armin Fuchs ◽  
Viktor K. Jirsa ◽  
J.A.Scott Kelso
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Hand Movements

Influence of dental magnets on human brain activity: A quantitative EEG analysis

2021 ◽  
Author(s):  
Angel Elias ◽  
Fathima Banu Raza ◽  
Anand Kumar Vaidyanathan ◽  
Padmanabhan Thallam Veeravalli
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Quantitative Eeg ◽  
Eeg Analysis ◽  
Quantitative Eeg Analysis
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