Implementation method for identifying motion models based on cerebral-cortex hemoglobin information

Abstract

The invention discloses an implementation method for identifying motion models based on cerebral-cortex hemoglobin information. A testee self-controls the starting and stopping time under four motion models; as for the change rate of cerebral-cortex hemoglobin concentration, the statistical analysis technique is adopted to analyze the statistical difference between oxyhaemoglobin and deoxyhemoglobin in various motion related areas of the cerebral cortex under different motion models, and based on the statistical characteristics, two relative motion trends of getting-up and sitting-down, as well as stair ascending and descending, are distinguished; and on this basis, the difference between oxyhaemoglobin and deoxyhemoglobin in various motion areas of the cerebral cortex is analyzed, together with the both-leg relative motion trends and the new statistical characteristics, the specific motion model is distinguished. The non-intrusive NIRS technology is adopted to record the cerebral-cortex hemoglobin concentration information of a human body without external stimulation or preliminary training, and restrictions on the test environment and the testee are less; the advantage of ideal time and space resolution is achieved; and the intelligence of walking aids is improved.

Description

technical field [0001] The invention relates to a method for realizing motion pattern recognition based on cerebral cortex hemoglobin information, which belongs to the direction of brain-computer interface. Background technique [0002] According to the total population of my country in the sixth national census and the second national sample survey of disabled persons, the total number of disabled people in my country and the number of disabled people of different types and grades at the end of 2010 were estimated: among them, the largest proportion of physical disabilities was 29.08 %. In addition, my country has gradually entered an aging society. In 2012, the number of elderly people aged 60 and above was 193.9 million, accounting for 14.3% of the total population; among them, the population aged 65 and above was 127.14 million, accounting for 9.4% of the total population. According to expert forecasts, by 2020, the proportion of China's population aged 60 and over will ...

AI Technical Summary

Problems solved by technology

[0004] (1) Implantable or semi-implantable brain-computer interface technology has made breakthroughs, but implanting micro-electrodes in the gray matter of the experimenter's brain or on the cerebral cortex under the dura mater may trigger immune responses and Callus; and there are psychological and ethical issues after implantation, not yet suitable for widespread use

[0005] (2) Non-invasive brain information testing techniques include electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and near-infrared spectrum brain function Imaging (NIRS) and other technologies; among them, fMRI and PET technology have high spatial resolution, but low temporal resolution, and the body is often confined to a static state during the test, which has great constraints; the application of MEG requires external magnetic field Fully shielded; so at present, EEG and NIRS technology are mainly used in the research and development of products to help the elderly and the disabled

However, in the study of brain-computer interface systems based on EEG signals, the commonly used methods based on visual evoked potentials (VEP) and event-related potentials (P300) require additional stimulation devices to provide stimulation to generate evoked potentials, and rely on human A certain feeling, such as vision, forces the experimenter to synchronize with external stimuli. Since 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

The temporal resolution of NIRS technology is higher than that of fMRI, lower than that of EEG; the spatial resolution is higher than that of EEG, lower than that of fMRI; if the NIRS technology is used to test brain biological information for pattern recognition, it is conducive to dynamic measurement in the natural environment, but the biological information Extraction rate is low

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