Query augmentation is a crucial technique for refining semantically imprecise
queries. Traditionally, query augmentation relies on extracting information
from initially retrieved, potentially relevant documents. If the quality of the
initially retrieved documents is low, then the effectiveness of query
augmentation would be limited as well. We propose Brain-Aug, which enhances a
query by incorporating semantic information decoded from brain signals.
BrainAug generates the continuation of the original query with a prompt
constructed with brain signal information and a ranking-oriented inference
approach. Experimental results on fMRI (functional magnetic resonance imaging)
datasets show that Brain-Aug produces semantically more accurate queries,
leading to improved document ranking performance. Such improvement brought by
brain signals is particularly notable for ambiguous queries.

利用从脑信号解码出的语义信息增强查询，Brain-Aug 生成原始查询的继续部分，通过排序导向的推理方法提高查询准确性，实验证明 Brain-Aug 使查询更准确，进而提高文档排序性能，对于歧义查询尤为显著。