Brain Dialogue Interface (BDI): A User-Friendly fMRI Model for Interactive Brain Decoding
Heng Huang, Lin Zhao, Zihao Wu, Xiaowei Yu, Jing Zhang, Xintao Hu,, Dajiang Zhu, Tianming Liu

TL;DR
This paper presents a user-friendly, unsupervised brain decoding model that enables interactive communication with the brain, effectively compresses brain signals, and integrates with multimodal inputs for versatile neurocognitive analysis.
Contribution
Introduces a novel interactive brain decoding model trained unsupervisedly, capable of dynamic dialogue-based decoding and efficient signal compression, surpassing traditional static models.
Findings
Successfully compresses brain signals from 185,751 voxels into 32 signals
Demonstrates feasibility of interactive, dialogue-based brain decoding
Integrates seamlessly with multimodal models for enhanced control
Abstract
Brain decoding techniques are essential for understanding the neurocognitive system. Although numerous methods have been introduced in this field, accurately aligning complex external stimuli with brain activities remains a formidable challenge. To alleviate alignment difficulties, many studies have simplified their models by employing single-task paradigms and establishing direct links between brain/world through classification strategies. Despite improvements in decoding accuracy, this strategy frequently encounters issues with generality when adapting these models to various task paradigms. To address this issue, this study introduces a user-friendly decoding model that enables dynamic communication with the brain, as opposed to the static decoding approaches utilized by traditional studies. The model functions as a brain simulator, allowing for interactive engagement with the brain…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces
