An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 2120 - 2131
• Main Authors: Liang, Sui, Kuang, Shaolong, Wang, Deheng, Yuan, Zhaohua, Zhang, Hongmiao, Sun, Lining
• Format: Journal Article
• Language: English
• Published: United States IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Brain computer interface; Brain modeling; Brain-Computer Interfaces; Classification; Computer applications; Data acquisition; Data augmentation; Data models; Decoders; Decoding; Deep learning; EEG; electroencephalogram; Electroencephalography; Electroencephalography - methods; Feature extraction; Frequency dependence; Generative adversarial networks; Human-computer interface; Humans; Imagination; Implants; Machine learning; motor imagery; Random noise; Signal classification; Synthesis; Training
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2023.3268979

While deep learning algorithms significantly improves the decoding performance of brain-computer interface (BCI) based on electroencephalogram (EEG) signals, the performance relies on a large number of high-resolution data for training. However, collecting sufficient usable EEG data is difficult due to the heavy burden on the subjects and the high experimental cost. To overcome this data insufficiency, a novel auxiliary synthesis framework is first introduced in this paper, which composes of a pre-trained auxiliary decoding model and a generative model. The framework learns the latent feature distributions of real data and uses Gaussian noise to synthesize artificial data. The experimental evaluation reveals that the proposed method effectively preserves the time-frequency-spatial features of the real data and enhances the classification performance of the model using limited training data and is easy to implement, which outperforms the common data augmentation methods. The average accuracy of the decoding model designed in this work is improved by (4.72±0.98)% on the BCI competition IV 2a dataset. Furthermore, the framework is applicable to other deep learning-based decoders. The finding provides a novel way to generate artificial signals for enhancing classification performance when there are insufficient data, thus reducing data acquisition consuming in the BCI field.