Multi-source manifold electroencephalogram feature transfer learning method

Abstract

The invention provides a multi-source manifold electroencephalogram feature transfer learning method. Firstly, distribution mean values of covariance matrixes of a source domain and a target domain are aligned in a symmetric positive definite (SPD) manifold, tangent space features are extracted, Grassmann manifold features are extracted through Grassmann manifold learning, and marginal probability distribution differences of the source domain and the target domain are minimized. After the popular features are obtained, performing classification model training on each source domain by taking structural 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, and integrating prediction results of different source domains in a voting manner. And finally, iteration is carried out to obtain a classification result of multi-source transfer learning.

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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