Source domain selection method for multi-source electroencephalogram migration

Abstract

The invention discloses a source domain selection method for multi-source electroencephalogram migration, which comprises the following steps of: firstly, extracting tangent space characteristics and Grassmann epidemic characteristics, and minimizing marginal probability distribution difference between a source domain and a target domain; after the popularity features are obtained, performing classification model training on each source domain by taking structure risk minimization and conditional probability distribution difference minimization of the source domain and the target domain as a target function, predicting the target domain by each classifier, integrating prediction results of different source domains in a voting manner, and after the first iteration, performing classification model training on the target domain; the method comprises the following steps of: respectively training a classifier for each source domain, and finally voting to generate a multi-source classifier, so that the condition of LSA is met, carrying out LSA once to obtain mobility estimation values of different source domains, removing k source domains, and in the subsequent iteration, only repeatedly training classifier iteration for the remaining source domains, thereby improving the operation efficiency.

Description

technical field [0001] The invention belongs to the research field of nervous system motion control mechanism, EEG signal preprocessing, EEG feature extraction, manifold feature alignment and extraction, multi-source transfer frame design, thereby performing multi-source EEG transfer learning. Background technique [0002] As the center for controlling human thoughts, behaviors, emotions and other activities, the brain analyzes and processes information obtained from the external environment, and communicates with the outside world through neuromuscular pathways. However, diseases such as spinal cord injury, amyotrophic lateral sclerosis, stroke, Parkinson's, and traumatic brain injury often damage or weaken the nerve center function, resulting in varying degrees of perception, sensation, speech, movement and other obstacles. On the one hand, a breakthrough in Brain Computer Interface (BCI) technology is expected to achieve functional compensation and functional reconstructi...

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Problems solved by technology

However, these Riemannian space-based methods are computationally intensive and incompatible with machine learning methods in Euclidean space

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