A Systematic Review of EEG-Based Visual Decoding Using Machine Learning and Deep Learning

Abstract

The primary objective of this research is to develop and validate robust computational frameworks capable of decoding complex cognitive processes—specifically visual perception and mental imagery—directly from non-invasive electroencephalography (EEG) signals. Translating raw, high-dimensional neural activity into coherent visual representations presents a significant challenge in computational neuroscience. To address this, the project leverages state-of-the-art deep generative learning models, including latent diffusion models and multimodal transformers. The methodology emphasizes rigorous neural data processing pipelines adhering to Brain Imaging Data Structure (BIDS) standards. By applying advanced signal processing techniques, such as FastICA for artifact removal and maximum relevance minimum redundancy (mRMR) feature selection on 40-channel and 64-channel EEG configurations, the project aims to effectively isolate relevant neural correlates. Ultimately, this research seeks to bridge the semantic gap between non-invasive neural recordings and visual reconstruction, optimizing both the spatial and temporal resolution of the decoded outputs. Expected Outcomes: Algorithmic Advancements: The creation of highly optimized, transformer-based architectures and diffusion models that significantly improve the accuracy and fidelity of reconstructing visual stimuli from EEG data. Standardized Processing Pipelines: The establishment of scalable, reproducible preprocessing frameworks utilizing BIDS standards for high-density EEG, facilitating better data sharing and cross-study validation within the computational neuroscience community. High-Impact Academic Contributions: The publication of comprehensive systematic reviews—identifying current methodologies and gaps in EEG-based visual decoding—alongside empirical research papers detailing the efficacy of the proposed generative architectures. Advancements in Brain-Computer Interfaces (BCI): Laying the foundational groundwork for next-generation, non-invasive BCIs. These advancements have the potential to drive future clinical and technological applications, particularly in developing assistive communication devices that translate mental imagery into visual media for individuals with motor or speech impairments.