关键词: 脑电图（EEG）, 径向基神经网络, 直推式迁移学习

Abstract: In epileptic electroencephalogram (EEG) signal recognition, traditional intelligent modeling method requires that the training data set and test data sets are subject to the same distribution. But in practical applications, some cases cannot meet this condition, which results in a sharp degeneration in the performance of traditional methods. In order to overcome the above challenge, by the introduction of transfer learning strategies, this paper proposes a transductive radial basis function neural network (TRBFNN) in order to cater for the migration environment of data distribution. The experimental results in epileptic EEG signals recognition show that the proposed TRBFNN has better adaptability for migration scenarios of data distribution and the recognition performance will not be sharp deterioration when there are differences between the distributions of the training data and test data.

Key words: electroencephalogram (EEG), radial basis function neural network, transductive transfer learning