A natural action electroencephalographic recognition method based on Riemannian geometry for classifying and recognizing electroencephalogram signals of natural actions. Multi-channel electroencephalogram signals are collected; channels having high interference are excluded after analysis and observation; zero-phase bandpass filtering is performed; the time domain of the electroencephalogram signals is intercepted according to natural action force information; a covariance matrix of the multi-channel electroencephalogram signals is calculated; the covariance matrix is projected onto a Riemannian tangent space with the Riemannian mean being the tangent point; and lastly, the electroencephalogram signals are classified in the Riemannian geometric tangent space using shrinkage linear discriminant analysis algorithms. The signal processing method provided is efficient, uses novel and efficient algorithms, is reliable, and has important application value.
NATURAL ACTION ELECTROENCEPHALOGRAPHIC RECOGNITION METHOD BASED ON RIEMANNIAN GEOMETRY