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Assessment and Communication for People with Disorders of Consciousness

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fMRI Brain Decoding and Its Applications in Brain-Computer Interface: A Survey.

Bing Du¹, Xiaomu Cheng¹, Yiping Duan²

¹School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

|February 25, 2022

Summary

This summary is machine-generated.

This review explores brain neural activity decoding using machine learning and deep learning, focusing on models like VAE, GAN, and GCN for brain-computer interfaces (BCI) and mental health applications.

Keywords:

brain decoding brain–computer interface (BCI)functional magnetic resonance imaging (fMRI)generative adversarial network (GAN)graph convolutional networks (GCN)variational autoencoder (VAE)

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Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Brain neural activity decoding is crucial for neuroscience and brain-computer interfaces (BCI).
Early decoding relied on linear models and traditional machine learning algorithms.
Deep learning has advanced visual stimuli reconstruction from fMRI data.

Purpose of the Study:

To review brain activity decoding models based on machine learning and deep learning.
To highlight advanced models: variational auto-encoder (VAE), generative adversarial network (GAN), and graph convolutional network (GCN).
To present fMRI-based BCI applications in mental and psychological disease treatment.

Main Methods:

Review of machine learning and deep learning algorithms for brain activity decoding.
Focus on VAE, GAN, and GCN models.
Analysis of fMRI data for brain decoding.

Main Results:

Deep neural networks (DNNs) show promise in reconstructing visual stimuli from fMRI.
VAE, GAN, and GCN are key models in current brain decoding research.
Brain decoding via fMRI-based BCI demonstrates potential in treating mental health disorders.

Conclusions:

Brain activity decoding is advancing rapidly with deep learning techniques.
The reviewed models (VAE, GAN, GCN) are pivotal for future BCI development.
fMRI-based BCI holds significant potential for neurological and psychological applications.