Motor imagery electroencephalogram voting strategy sorting method based on extreme learning machines

Abstract

The invention belongs to the field of mode recognition and a brain-machine interface and discloses a motor imagery electroencephalogram voting strategy sorting method based on extreme learning machines. The motor imagery electroencephalogram voting strategy sorting method comprises the following steps: dividing an original motor imagery electroencephalogram into S sections of sub-signals; carrying out dimensionality reduction on each section of sub-signal by a principal component analysis method; carrying out secondary dimensionality reduction on a feature vector subjected to the dimensionality reduction by a linear discrimination analysis method; carrying out the same processing on the S sections of sub-signals to finally obtain S (K-1)-dimensional feature vectors, and combining the S (K-1)-dimensional feature vectors to finally obtain an S*(K-1)-dimensional feature; and transmitting the S*(K-1)-dimensional feature into a plurality of ELM (Extreme Learning Machine) sorting devices so as to obtain a final sorting result by utilizing a voting sorting strategy. The invention provides a voting sorting strategy based on the ELMs; compared with a traditional multi-time ELM average accuracy scheme, the method provided by the invention has the advantages that the sorting accuracy is improved under the condition of not influencing the training sorting low consumption.

Description

technical field [0001] The invention belongs to the fields of pattern recognition and Brain-Computer Interface (Brain-Computer Interface, BCI), and relates to a method for classifying motor imagery EEG signals in a Brain-Computer Interface system device, specifically, extracting feature vectors A method for classification with extremely fast learning machine-based voting strategies. Background technique [0002] There are many diseases that affect the communication between the brain and the external environment, such as paralysis. These diseases will cause patients to lose part or all of their autonomic control, which will bring a very heavy burden to the family and society. With the development of computer science and the deepening of scientists' research on brain function, people began to try to establish a new way to transmit information and commands between the brain and the external environment, and do not rely on the communication and control pathways of muscle and ne...

AI Technical Summary

Problems solved by technology

Since the internal parameters of ELM are randomly generated, the predicted category of a single sample also has strong randomness. The common method is to average the classification results multiple times, but it does not reduce the randomness of the predicted category of the sample.

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