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Mixed feature-based fine imaginary action online brain-computer interface method

A hybrid feature, machine interface technology, used in electrotherapy, computer parts, mechanical mode conversion, etc., can solve the problems of expensive surgery, not widely used, high infection risk, etc., to improve the recognition accuracy, improve The classification accuracy rate and the effect of preventing muscle atrophy

Inactive Publication Date: 2018-01-09
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this invasive method needs to bear expensive surgical expenses and high risk of infection, and has not been widely used in practice.

Method used

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  • Mixed feature-based fine imaginary action online brain-computer interface method
  • Mixed feature-based fine imaginary action online brain-computer interface method
  • Mixed feature-based fine imaginary action online brain-computer interface method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] An Online Brain-Computer Interface Method for Fine Imaging Actions Based on Hybrid Features, see figure 1 , the method includes the following steps:

[0039] 101: Four types of actions are designed for different joints of the right upper limb, including: fisting, wrist raising, elbow flexion and shoulder abduction; by placing four types of movements on the palm flexor, wrist median nerve, arm biceps brachii and arm deltoid The electrodes at the same position give electrical stimulation at the beginning of the prompt, and the subjects choose to pay attention to the location of the electrical stimulation while imagining the corresponding action;

[0040] 102: Effectively integrate the event-related desynchronization features of different frequency distributions and the steady-state somatosensory evoked potential features induced by electrical stimulation to form a hybrid paradigm;

[0041] 103: Use an algorithm based on multi-frequency component spatial filtering to extr...

Embodiment 2

[0047] Combined with the specific calculation formula, Figure 1-Figure 3 , the example further introduces the scheme in embodiment 1, see the following description for details:

[0048] 201: The design of different joints of the right upper limb includes four types of movements including fist clenching, wrist raising, elbow flexion and shoulder abduction, by placing four muscles in the palm flexor, wrist median nerve, biceps brachii and deltoid muscles of the arm The electrodes at the same position give electrical stimulation at the beginning of the prompt, and the subjects choose to pay attention to the location of the electrical stimulation while imagining the corresponding action;

[0049] Each person conducts 8 sets of experiments each time, each set of experiments includes 40 single tasks, four types of actions appear randomly, and each 10 single tasks / group. Before formally collecting EEG signals, each subject was required to perform imaginative movement training 3 day...

Embodiment 3

[0066] The embodiment of the present invention further introduces the process of applying stimulation and collecting EEG signals in Embodiment 1-2 in conjunction with the accompanying drawings, see the following description for details:

[0067] The embodiment of the present invention designs four types of actions for different joints of the right upper limb, including: making a fist, raising the wrist, bending the elbow and shoulder abduction. Then, the electrodes placed on the four positions of palmar flexor, wrist median nerve, arm biceps brachii and arm deltoid were simultaneously given electrical stimulation at the beginning of the cue, and the subjects chose to pay attention to the location of the electrical stimulation while imagining the corresponding action. Finally, through the process of feature extraction, pattern recognition, etc., the decision value is output and the voice prompts are fed back to the subjects in time. The structural schematic diagram of the embod...

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Abstract

The invention discloses a mixed feature-based fine imaginary action online brain-computer interface method. The method comprises the steps of designing four actions of making a fist, raising the wrists, bending the elbows and abducing the shoulders for different joints of the upper limb on the right side; giving electrical stimulation during prompting of starting through electrodes placed on flexor muscles of the palms, median nerves of the wrists, bicipital muscles of the arms and deltoid muscles, paying selective attention to electrical stimulation positions while imagining the correspondingactions by a testee; effectively fusing event-related desynchronization features of different frequency distribution and electrical stimulation induced steady state somatosensory evoked potential features to form a mixed normal form; by adopting a multi-frequency component spatial filtration-based algorithm, extracting the event-related desynchronization and steady state somatosensory evoked potential features from the mixed normal form, thereby increasing the correct identification rate; and performing mode identification by adopting a multi-classification support vector machine, outputtinga decision value, and feeding back a voice prompt to the testee. The body parts controlled by high-correlation adjacent brain regions can be effectively distinguished; and the method has excellent performance.

Description

technical field [0001] The invention relates to the field of online brain-computer interface, in particular to an online brain-computer interface method based on mixed features of fine imaginary actions. Background technique [0002] Brain-computer interface (Brain-Computer Interface, BCI) system is a system that realizes direct control of external devices by brain signals by collecting and analyzing brain signals and converting them into output instructions. In the current BCI system, various types of brain signals are collected and analyzed, so as to control external devices as output instructions. According to the different acquisition methods of these brain signals, they can be divided into invasive and non-invasive. [0003] Invasive methods include two types: one is cortical EEG (Electrocorticography, ECoG), the signal is collected by subdurally implanted electrodes, which can record neural activity in a smaller area, with higher signal-to-noise ratio and spatial reso...

Claims

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Application Information

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IPC IPC(8): G06F3/01A61N1/36A61N1/08
Inventor 陈志堂赵欣明东奕伟波王坤许敏鹏何峰
Owner TIANJIN UNIV
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