Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition

摘要

Motor imagery (MI) is an important control paradigm in the field of brain-computer interface(BCI), which enables the recognition of personal intention. So far, numerous methods have beendesigned to classify EEG signal features for MI task. However, deep neural networks have beenseldom applied to analyze EEG signals. In this study, two novel kinds of deep learning schemesbased on convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) were proposedforMI-classification. The frequency domain representations of EEG signals were obtainedusing short time Fourier transform (STFT) to train models. Classification results were comparedbetween conventional algorithm, CNN, and LSTM models. Compared with two other methods,CNN algorithms had shown better performance. These conclusions verified that CNN methodwas promising for MI-based BCIs.