Privacy-Preserving Network BMI Decoding of Covert Spatial Attention

The brain-machine interface (BMI) has attracted much attention in the fields of biomedical engineering and ICT human communications. Of particular interest, neural decoding methods have rapidly developed over the last decade in neuroscience, allowing us to estimate the contents of human perception and subjective mental states by capturing brain activity patterns. However, the development of neural decoding will generate significant concern about privacy violation. In this manuscript, we propose a secure network BMI decoding method based on sparse coding for a covert spatial attention task. It is shown that secure sparse coding enables us to not only protect observed EEG signals, but also achieve the same estimation performance as that offered by sparse coding with unprotected observed signals.