关键词: 脑电图（EEG）, 小波变换, 癫痫识别, 迁移学习, 特征提取

Abstract: Intelligent recognition of electroencephalogram (EEG) signals is an important means of epilepsy detection. Almost all the traditional intelligent recognition methods assume that the training samples and the testing samples of EEG signals are drawn from same distribution. However in many practical applications for epilepsy detection, the assumption is too harsh, and it is invalid when there are some differences between the distribution of the training and testing data. In order to overcome this challenge, this paper introduces the widely concerned transfer learning method into EEG signals recognition. With the transfer learning based method, the distribution between the training and testing data is not required to be the same, which expands the application scene of the algorithm and realizes the adaptive recognition for EEG signals in the data drift scene. The experimental results show that the new method for epilepsy detection has a better adaptability than the traditional methods.

Key words: electroencephalogram (EEG), wavelet transform, epilepsy detection, transfer learning, feature extraction