中文摘要：

脑电信号分类问题是脑-机接口应用中的重点,而脑电信号分类的关键问题是如何寻找合适的特征.目前虽然有支持向量机、浅层神经网络等方法可以对脑电信号有效的分类,但是这些方法大都需要大量先验知识寻找数据的特征.由于脑电信号容易受到噪声干扰,尤其是在烟瘾渴求这种高级认知过程中,不同被试个体间具有很大差异,很难找到具有代表性的有效特征,脑电分类的准确率很难提高.为解决以上问题,本文采用一种基于卷积神经网络的方法对烟瘾患者在不同烟瘾渴求状态下的脑电信号的进行分类,与传统分类方法比,卷积神经网络不需要手动提取特征,能够直接训练原始的脑电信号数据,可以满足在实时反馈的烟瘾治疗过程对获取分类结果的快速需求.

英文摘要：

Electroencephalography （EEG） classification is the key point of brain-computer interface application. How to find effective feature is the major issues in EEG classification. Although several effective methods like support vector machines or neural networks have already been applied to EEG classification, but these methods need a large amount of prior knowledge to find the features of the data. Since the brain electrical signal appears to be more susceptible to noise interference and there are wide individual differences, so that effective features are difficult to been found. Meanwhile, it is difficult to improve the accuracy of the EEG classification, especially in the advanced cognitive process in the cigarette craving. In order to solve this problem, we use convolution neural networks （CNN） to classify EEG of cigarette craving patients under different status of cigarette craving. Compared with the traditional method, CNN does not need to manually extract features. It can directly train the original EEG data. More importantly, it can satisfy the demand which is to obtain the real-time feedback in the cigarette craving treatment process for classification results.