Cerebrum functional zone specificity electroencephalogram detecting method based on vector multi-classification

Abstract

The invention relates to a cerebrum functional zone specificity electroencephalogram detecting method based on vector multi-classification. The cerebrum functional zone specificity electroencephalogram detecting method is characterized by including the following steps: firstly, in the state that a detected person executes or receives a specificity task, electroencephalogram signals are collected; then a vector multi-classification method is adopted, wavelet decomposition is carried out on the electroencephalogram signals, all single sub-frequency-band signals are reconstituted, the energy differences of the single sub-frequency-band signals before and after a task event is executed serve as characteristic quantities, and multi-dimensional time-frequency domain vectors are constructed; and finally, a support vector machine multi-classification algorithm based on a decision-making tree is adopted for classifying the multi-dimensional time-frequency domain vectors, so that the specificities of electrodes when the electroencephalogram signals are led to the electrodes are identified, and cerebral cortex functional zone specificity detection can be completed. By means of the cerebrum functional zone specificity electroencephalogram detecting method, the electroencephalogram specificities of the cerebral cortex functional zone can be scientifically and accurately detected; and the method can be widely applied to studying and clinic application of neurosciences such as intra-operation cerebrum function positioning, cognitive function evaluation and cerebrum-computer interfaces based on electroencephalogram analysis.

Description

technical field [0001] The invention relates to the field of neuroscience research and clinical application based on EEG analysis, in particular to a method for detecting EEG specific to brain functional areas based on vector multi-classification. Background technique [0002] Research in life sciences shows that EEG signal analysis is one of the important ways for humans to understand the human nervous system. Various organs and tissue activities of the human body are accompanied by physiological electrical phenomena, and EEG is the overall reflection of the electrical physiological activities of central nerve cells on the surface of the cerebral cortex or scalp. Based on the working principle of the reflex arc of the nervous system, the detection and analysis of EEG signals can determine the state of various organs and tissues of the human nervous system; and the detection and analysis of EEG signals have been applied to medical clinical diagnosis and treatment (such as F...

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Problems solved by technology

[0005] At present, there is no report of a specific EEG detection method based on vector multi-classification at home and abroad; at the same time, there is no clinical application of a brain function-specific EEG detection method based on vector multi-classification at home and abroad.

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