Time–frequency transform based EEG data augmentation for brain–computer interfaces

Accurate decoding of electroencephalography (EEG) signals is crucial for brain–computer interfaces (BCIs); however, individual differences, non-stationarity of EEG signals, and limited training data make the decoding very challenging. Existing EEG data augmentation approaches usually operate in the temporal, frequency, or spatial domain only, which may not adequately capture the non-stationarity of EEGs. Moreover, these methods typically generate within-subject augmented trials, restricting their effectiveness in accommodating inter-subject variability. This paper proposes two time–frequency transform based EEG data augmentation approaches: Discrete Wavelet Transform Augmentation (DWTaug) and Hilbert–Huang Transform Augmentation (HHTaug). Both follow three steps: time–frequency domain decomposition, cross-subject sub-signal reassembling, and time domain reconstruction. Augmenting data expands the pool of labeled training samples, alleviating the data scarcity problem; time–frequency decomposition captures the non-stationary properties of EEG signals more effectively; finally, cross-subject reassembling of sub-signals handles individual differences. Experiments on 17 datasets from three different BCI paradigms demonstrated the superiority of DWTaug and HHTaug over nine existing EEG data augmentation approaches, improving 4% over baseline on average. By leveraging essential time–frequency information, DWTaug and HHTaug introduce new utility to traditional signal processing techniques, enhancing EEG data augmentation, thus effectively addressing key EEG decoding challenges. To our knowledge, this is the first work to simultaneously address individual variability, non-stationarity, and data scarcity in EEG decoding, significantly enhancing the real-world applicability of BCIs. Our code is publicized at https://github.com/wzwvv/CSDA.