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A Multimodal Brain Switch Detection Method Based on SSVEP and P300

A detection method and brain switch technology, applied in the direction of mechanical mode conversion, input/output of user/computer interaction, computer components, etc., can solve the problem of lack of idle state detection ability, etc. The effect of high accuracy

Active Publication Date: 2017-02-22
华南脑控(广东)智能科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these inventions are all synchronous brain-computer interfaces, that is, they do not have the ability to detect idle states.

Method used

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  • A Multimodal Brain Switch Detection Method Based on SSVEP and P300
  • A Multimodal Brain Switch Detection Method Based on SSVEP and P300
  • A Multimodal Brain Switch Detection Method Based on SSVEP and P300

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Embodiment Construction

[0066] The abbreviations involved in the method of the present invention are explained as follows:

[0067] Brain computer interface: (brain computer interface, BCI) refers to the direct communication and control channel established between the human brain and computers or other electronic devices, which does not depend on the normal physiological output pathways of the brain (peripheral nervous system and muscle tissue) ), which is a brand-new man-machine interface method.

[0068] Scalp EEG: Electroencephalogram (EEG) is the potential performance of brain activity on the scalp.

[0069] P300 signal: P300 is an endogenous special event-related potential (eventrelated potentials, ERP) related to cognitive function, and its peak value appears about 300ms after the related event occurs.

[0070] Steady-state visual evoked potential SSVEP: Visual evoked potential (VEP) refers to the specific electrical activity generated by the nervous system receiving visual stimuli (such as fl...

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Abstract

The invention discloses a detecting method for a multi-modal brain switch based on SSVEP and P300. The method comprises the following steps: generating a scalp electroencephalogram signal by a user according to a working interface command in a display device; collecting the scalp electroencephalogram signal by an electrode cap, and transferring the signal by an I / O interface module of a computer to a signal processing module in the computer after the signal is converted by a digital-to-analogue conversion module and is amplified by a signal amplifier; copying the scalp electroencephalogram signal into two copies, and respectively performing P300 electric potential detection and SSVEP detection; classifying the control state and the idle state by combining the respective detection output of the P300 electric potential and the SSVEP, and determining goals. For the detecting method, the problem that pages with complex contents cannot be browsed for a browser based on the non-mouse control can be solved, the control speed is greatly improved, and the precision is greatly improved.

Description

technical field [0001] The invention relates to the field of brain-computer interface, in particular to a multi-modal brain switch detection method based on SSVEP and P300. Background technique [0002] Brain computer interface (BCI) refers to the direct communication and control channel established between the human brain and computers or other electronic devices, which does not depend on the normal physiological output pathways of the brain (peripheral nervous system and muscle tissue) , is a brand-new human-computer interface method, and is a hot topic in brain function research in recent years. Currently, there are two types of brain-computer interface technologies: invasive and non-invasive. The signal accuracy obtained by the invasive brain-computer interface is relatively high, the signal-to-noise ratio is high, and it is easy to analyze and process. There is a greater risk of infection or injury. Although the brain signal obtained by the non-invasive brain-compute...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F3/01
Inventor 李远清潘家辉
Owner 华南脑控(广东)智能科技有限公司