In visual-imagery-based brain–computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor—uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.

基于视觉图像的脑机接口（VI-BCI）存在想象任务单一、特征信息描述不足等问题，严重阻碍了VI-BCI技术在恢复通信领域的发展和应用。在本文中，我们设计并优化了一种基于视觉图像（VI）脑电图（EEG）信号的多字符分类方案，用于对29个字符进行分类，其中包括26个小写英文字母和3个标点符号。首先，设计了一种随机呈现字符并包括准备阶段的新范式来获取脑电图信号并构建多字符数据集，这可以消除VI任务之间的影响。其次，通过Morlet小波变换获得张量数据，并采用基于张量-不相关多线性主成分分析的特征提取算法来提取高质量的特征。最后采用支持向量机、K近邻、极限学习机三种分类器对多字符进行分类，并对结果进行比较。实验结果表明，该方案有效地提取了冗余最小、相关性弱、表征能力强的字符特征，成功实现了29个字符的平均分类准确率97.59%，在分类准确率和分类数量上均优于现有研究。 。本研究设计了一种获取VI脑电信号的新范式，并结合Morlet小波变换和UMPCA算法来提取字符特征，从而能够在各种分类器中进行多字符分类。这项研究为建立大脑与世界的直接交流铺平了一条新途径。