Visual Representation Model for fMRI-based Brain Decoding

Saengpetch, Piyawat; Pipanmemekaporn, Luepol; Kamolsantiroj, Suwatchai

doi:10.1145/3324033.3324051

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Brain decoding is presented utilizing semantic features to obtain the label set for new input data. Regular data sets obtained from experience, meaning for describing the most likely concepts and is learned by using an fMRI subset for predicting the feature values, whereas the forecast features are used for decoding the correlated object [10].…”

Section: Representation Models Of Brain Decodingmentioning

confidence: 99%

Functional magnetic resonance imaging-based brain decoding with visual semantic model

Saengpetch

Pipanmemekaporn

Kamolsantiroj

2020

IJECE

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The activity pattern of the brain has been activated to identify a person in mind. Using the function magnetic resonance imaging (fMRI) to decipher brain decoding is the most accepted method. However, the accuracy of fMRI-based brain decoder is still restricted due to limited training samples. The limitations of the brain decoder using fMRI are passed through the design features proposed for many label coding and model training to predict these characteristics for a particular label. Moreover, what kind of semantic features for deciphering the neurological activity patterns are unclear. In current work, a new calculation model for learning decoding labels that is consistent with fMRI activity responses. The approach demonstrates the proposed corresponding label's success in terms of accuracy, which is decoded from brain activity patterns and compared with conventional text-derived feature technique. Besides, experimental studies present a training model based on multi-tasking to reduce the problems of limited training data sets. Therefore, the multi-task learning model is more efficient than modern methods of calculation, and decoding features may be easily obtained.

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Section: Representation Models Of Brain Decodingmentioning

confidence: 99%

Functional magnetic resonance imaging-based brain decoding with visual semantic model

Saengpetch

Pipanmemekaporn

Kamolsantiroj

2020

IJECE

Self Cite

View full text Add to dashboard Cite

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A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

et al. 2022

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Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.

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Visual Representation Model for fMRI-based Brain Decoding

Cited by 2 publications

References 8 publications

Functional magnetic resonance imaging-based brain decoding with visual semantic model

Functional magnetic resonance imaging-based brain decoding with visual semantic model

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

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