Pan Hongguang, Li Zhuoyi, Tian Chen, Wang Li, Fu Yunpeng, Qin Xuebin, Liu Fei
College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 Shaanxi China.
Shaanxi Broadcasting Corporation, Xi'an, 710061 Shaanxi China.
Cogn Neurodyn. 2023 Apr;17(2):373-384. doi: 10.1007/s11571-022-09819-w. Epub 2022 Jun 26.
Brain-computer interface (BCI) can obtain text information by decoding language induced electroencephalogram (EEG) signals, so as to restore communication ability for patients with language impairment. At present, the BCI system based on speech imagery of Chinese characters has the problem of low accuracy of features classification. In this paper, the light gradient boosting machine (LightGBM) is adopted to recognize Chinese characters and solve the above problems. Firstly, the Db4 wavelet basis function is selected to decompose the EEG signals in six-layer of full frequency band, and the correlation features of Chinese characters speech imagery with high time resolution and high frequency resolution are extracted. Secondly, the two core algorithms of LightGBM, gradient-based one-side sampling and exclusive feature bundling, are used to classify the extracted features. Finally, we verify that classification performance of LightGBM is more accurate and applicable than the traditional classifiers according to the statistical analysis methods. We evaluate the proposed method through contrast experiment. The experimental results show that the average classification accuracy of the subjects' silent reading of Chinese characters "(left)", "(one)" and simultaneous silent reading is improved by 5.24%, 4.90% and 12.44% respectively.
脑机接口（BCI）可以通过解码语言诱发脑电图（EEG）信号来获取文本信息，从而恢复语言障碍患者的沟通能力。目前，基于汉字语音想象的BCI系统存在特征分类准确率低的问题。本文采用轻量级梯度提升机（LightGBM）来识别汉字并解决上述问题。首先，选择Db4小波基函数对全频段进行六层分解，提取具有高时间分辨率和高频率分辨率的汉字语音想象相关特征。其次，利用LightGBM的两个核心算法，基于梯度的单边采样和排他特征捆绑，对提取的特征进行分类。最后，根据统计分析方法验证了LightGBM的分类性能比传统分类器更准确、更适用。我们通过对比实验对所提方法进行评估。实验结果表明，受试者默读汉字“（左）”“（一）”以及同时默读时的平均分类准确率分别提高了5.24%、4.90%和12.44%。
