  Electroencephalogram (EEG) classification has been widely used in various
medical and engineering applications, where it is important for understanding
brain function, diagnosing diseases, and assessing mental health conditions.
However, the scarcity of EEG data severely restricts the performance of EEG
classification networks, and generative model-based data augmentation methods
emerging as potential solutions to overcome this challenge. There are two
problems with existing such methods: (1) The quality of the generated EEG
signals is not high. (2) The enhancement of EEG classification networks is not
effective. In this paper, we propose a Transformer-based denoising diffusion
probabilistic model and a generated data-based data augmentation method to
address the above two problems. For the characteristics of EEG signals, we
propose a constant-factor scaling method to preprocess the signals, which
reduces the loss of information. We incorporated Multi-Scale Convolution and
Dynamic Fourier Spectrum Information modules into the model, improving the
stability of the training process and the quality of the generated data. The
proposed augmentation method randomly reassemble the generated data with
original data in the time-domain to obtain vicinal data, which improves the
model performance by minimizing the empirical risk and the vicinal risk. We
experiment the proposed augmentation method on five EEG datasets for four tasks
and observe significant accuracy performance improvements: 14.00% on the Bonn
dataset; 25.83% on the New Delhi epilepsy dataset; 4.98% on the SleepEDF-20
dataset; 9.42% on the FACED dataset; 2.5% on the Shu dataset. We intend to make
the code of our method publicly accessible shortly
