Brain-computer interface decoding method based on intracranial electroencephalogram and scalp electroencephalogram fusion

A technology of brain-computer interface and decoding method, which is applied in medical science, sensors, diagnostic recording/measurement, etc., can solve the problems of inability to change the position, poor scalability, etc., and achieve good rehabilitation effect, high classification accuracy, and robustness Good results

Pending Publication Date: 2022-01-21
ZHEJIANG LAB +1
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AI Technical Summary

Problems solved by technology

However, the acquisition electrodes of intracranial signals can only detect brain activity in a small area, and may only have a good performance in decoding part of the motion intention.
In addition, the position of the collection electrode cannot be changed after implantation, and the scalability is poor

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  • Brain-computer interface decoding method based on intracranial electroencephalogram and scalp electroencephalogram fusion
  • Brain-computer interface decoding method based on intracranial electroencephalogram and scalp electroencephalogram fusion
  • Brain-computer interface decoding method based on intracranial electroencephalogram and scalp electroencephalogram fusion

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Embodiment Construction

[0030] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manners of the present invention will be further described in detail below.

[0031] Such as figure 1 As shown, a brain-computer interface decoding method based on the fusion of intracranial EEG and scalp EEG of the present invention mainly includes: synchronously collecting intracranial EEG signals (LFP) and scalp EEG signals (EEG), EEG signal pre-processing processing, feature extraction of EEG signals, and joint decoding of EEG signals. Specifically include the following steps:

[0032] 1. Synchronously collect intracranial EEG signals (LFP) and scalp EEG signals (EEG).

[0033] A 52-lead EEG cap was used to collect the EEG signals of the subjects at a sampling frequency of 1 kHz. Among them, the electrode distribution conforms to the international 10-20 system, and CPz is used as the reference electrode. The specific electrodes and their positions a...

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Abstract

The invention discloses a brain-computer interface decoding method based on intracranial electroencephalogram and scalp electroencephalogram fusion. The brain-computer interface decoding method comprises the steps of synchronously collecting intracranial electroencephalogram signals and scalp electroencephalogram signals, preprocessing the electroencephalogram signals, extracting electroencephalogram signal features and jointly decoding the electroencephalogram signals. The motor imagery intention of a user is decoded by fusing the characteristics of an intracranial local field potential signal LFP and a scalp electroencephalogram EEG signal, the classification accuracy is high, the robustness is good, and accurate real-time feedback can be provided in rehabilitation training based on a brain-computer interface, so that a good rehabilitation effect is achieved, and a thought and a method are provided for combined application of the intracranial electroencephalogram signals and the scalp electroencephalogram signals.

Description

technical field [0001] The invention belongs to the field of biomedical engineering, and in particular relates to a brain-computer interface decoding method based on fusion of intracranial brain electricity and scalp brain electricity. Background technique [0002] At present, the number of disabled people with impaired motor function is on the rise, and rehabilitation training is the most common method to assist patients in recovering their motor ability. Compared with the traditional passive rehabilitation training, the active rehabilitation training based on the brain-computer interface (BCI) uses the patient to spontaneously generate the movement intention of the rehabilitation training, and converts the intention into instructions to control external equipment to assist the patient to complete the training action. . BCI techniques can increase patient engagement and are believed to be more effective at inducing neuroplasticity and achieving better recovery. Therefore,...

Claims

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Application Information

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IPC IPC(8): A61B5/369A61B5/372
CPCA61B5/369A61B5/372A61B5/7203A61B5/7228A61B5/7225A61B5/7264
Inventor 孙煜冯钊钱霖泽
Owner ZHEJIANG LAB
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