Identification method of movement velocity state based on cerebrum hemoglobin information

Abstract

The invention discloses an identification method of a movement velocity state based on cerebrum hemoglobin information. The method comprises the first step of making a testee autonomously execute riding movement under low-velocity, middle-velocity and high-velocity states; the second step of aiming at cortex hemoglobin concentration information recorded in the movement initial time, adopting a difference value of oxygen-containing hemoglobin and deoxygenated hemoglobin as an analysis parameter, and considering parameter characteristics of key channels via four frequency bands respectively based on a change rate mean value of the corresponding difference value; the third step of identifying three different kinds of velocity states. The change rate mean value of the difference value of the oxygen-containing hemoglobin and deoxygenated hemoglobin of the key channels under four frequency bands is directly utilized as a feature vector, and an extreme learning machine ELM algorithm is adopted to identify velocity grades.

Description

technical field [0001] The invention belongs to the technology of intelligent walking aid and rehabilitation training, and in particular relates to a realization method for identifying the movement speed state of lower limbs based on cerebral cortex hemoglobin information. Background technique [0002] According to statistics from the Disabled Persons' Federation, the number of disabled people in my country is about 85 million, of which the number of physically disabled people accounts for 29.08%. Among them, there are more and more lower limb walking obstacles caused by stroke and traumatic brain injury. Among them, only stroke is newly diagnosed every year. The number of patients has reached about 2 million, and about 70% to 80% of patients cannot live independently due to disabilities. Their physical impairments have brought a great burden to the family and society. Therefore, the prognosis and rehabilitation of these patients with physical impairments is very important. D...

AI Technical Summary

Problems solved by technology

However, in the study of brain-computer interface systems based on EEG signals, the commonly used methods based on visual evoked potential (VEP) and event-related potential (P300) require additional stimulation devices to provide stimulation to generate evoked potentials, and rely on human Some kind of feeling (such as vision), forcing the experimenter to synchronize with external stimuli, because long-term operation is likely to cause visual fatigue or reduce the significance of P300 potential, the corresponding brain-computer interface operation time should not be too long

The spontaneous EEG depends on the user's spontaneous mental activity. Only a special thinking process can produce detectable brain activity. It requires a lot of training by the experimenter to generate a specific pattern of EEG, which is greatly affected by subjective factors.

Therefore, most experiments are completed under specific conditions, requiring the experimenter to concentrate, the realized actions are simple and limited, lack of naturalness and flexibility, and are not very practical

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