Deep Learning for EEG-Based Visual Classification and Reconstruction: Panorama, Trends, Challenges and Opportunities

Area of Science:

• Neuroscience
• Computer Science
• Artificial Intelligence

Background:

• Electroencephalogram (EEG)-based visual classification and reconstruction is an emerging research area with growing attention.
• Deep learning methods have shown significant promise in advancing this field.
• A comprehensive review of deep learning methodologies for EEG-based visual tasks is currently lacking in the literature.

Purpose of the Study:

• To provide the first comprehensive review of deep learning methodologies applied to EEG-based visual classification and reconstruction.
• To systematically analyze existing deep learning approaches from feature encoding and decoding perspectives.
• To discuss challenges and future opportunities in this research domain.

Main Methods:

• Comprehensive summarization and systematic analysis of representative deep learning methods.
• Introduction of benchmark datasets, experimental paradigms, and performance evaluations.
• Exploration of methodological essences, neuroscientific insights, and their dynamic interaction.

Main Results:

• An in-depth analysis of deep learning techniques for EEG-based visual tasks.
• An overview of available datasets and their associated experimental setups.
• Identification of key insights and potential avenues for technological innovation.

Conclusions:

• This review serves as a foundational resource for researchers in EEG-based visual analysis.
• It highlights the critical role of deep learning in advancing the field.
• The work aims to guide future research directions and foster academic breakthroughs.