传统的稳态视觉诱发电位（steady-state visual evoked potential, SSVEP）脑机接口系统通常使用少量频率进行编码，导致编码数量限制在几十个，无法满足有大量指令需求的环境作业。为了解决这一问题，提出一种基于海明距离的多频编码（Hamming distance multi-frequency code, HDMFC）范式及相应的识别算法，将海明距离同刺激范式编码和信号识别算法结合，利用8个频率信号可编码120个指令，并对7名受试者进行数据采集和分类实验。结果表明，基于海明距离的120编码在线实验准确率可达90.60%。研究成果为SSVEP范式编码数量的增加和分类效果的提升提供了有效的方法，验证了海明距离在这一领域的实用性和有效性。

The traditional steady-state visual evoked potential (SSVEP) brain computer interface system usually uses a small number of frequencies for encoding, resulting in a limited number of encodings to dozens, which cannot meet the demands of environmental tasks with a large number of instructions. To address this issue, a Hamming distance multi frequency code (HDMFC) paradigm and corresponding recognition algorithm based on Hamming distance were proposed. The Hamming distance was combined with stimulus paradigm encoding and signal recognition algorithms to encode 120 instructions using 8 frequency signals. Data collection and classification experiments were conducted on 7 subjects. The results show that the accuracy of the 120 encoding online experiment based on Hamming distance can reach 90.60%. The research results provide an effective method for increasing the number of SSVEP paradigm codes and improving classification performance, verifying the practicality and effectiveness of Hamming distance in this field.