Data collection, enhancement, and classification of functional near-infrared spectroscopy motor execution and imagery

Recognition and execution of motor imagery play a key role in brain-computer interface (BCI) and are prerequisites for converting thoughts into executable instructions. However, to date, data acquired through commonly used electroencephalography (EEG) methods are very sensitive to motion interference, which will affect the accuracy of the data classification. The emerging functional near-infrared spectroscopy (fNIRS) technique, while overcoming the drawbacks of EEG's susceptibility to interference and difficulty in detecting motor signals, has less publicly available data. In this paper, we designed a motor execution and imagery experiment based on a wearable fNIRS device to acquire brain signals and proposed a modified Kolmogorov-Arnold network (named SE-KAN) for recognizing fNIRS signals corresponding to the task. Due to the small number of subjects in this experiment, the Wasserstein generative adversarial network was used to enhance the data processing. For the fNIRS data recognition task, the SE-KAN method achieved 96.36 ± 2.43% single-subject accuracy and 84.72 ± 3.27% cross-subject accuracy. It is believed that the dataset and method of this paper will help the development of BCI.