A coding-decoding method for a brain-machine interface system based on asymmetric electroencephalographic features. The brain-machine interface comprises a stimulation-inducing module, an electroencephalographic signal collecting module, an electroencephalographic data module constituted by a training set Xk and a test sample Y, and an electroencephalographic signal decoding module. The stimulation-inducing module transmits as required a mix coded visual stimulation to a testee to induce a corresponding electroencephalographic signal. The collecting module forms data information by amplifying and filtering the electroencephalographic signal so as to constitute an electroencephalographic data module. The decoding module decodes electroencephalographic data and converts same into an instruction set for output. The method, combined with mixed coding strategies such as space-division multiple access, code-division multiple access, frequency-division multiple access, and phase-division multiple access, effectively expands the number of system instruction sets on the basis of the asymmetric features of electrophysiological activities of the brain in response to a stimulation; at the same time, the decoding method provided increases the signal-to-noise ratio of the electroencephalographic signal itself, thus increasing the classification recognition efficiency for signal features.
CODING-DECODING METHOD FOR BRAIN-MACHINE INTERFACE SYSTEM BASED ON ASYMMETRIC ELECTROENCEPHALOGRAPHIC FEATURES