A Brain-Computer Interface Lead Optimization Method Based on Independent Component Analysis

A technology of independent component analysis and optimization method, applied in the field of brain-computer interface, it can solve the problems of high complexity, high requirements on processor computing performance, long time, etc., and achieve the effect of alleviating the impact, improving stability and practicability

Inactive Publication Date: 2019-01-04
ANHUI UNIVERSITY
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Problems solved by technology

However, this type of algorithm has high computational complexity, long calculation time, and high requirements for the computing performance of the processor.
[0005] To sum up, there are currently few optimization methods for ICA-MIBCI leads, while the optimization methods for other MIBCI systems have problems such as algorithm instability, high complexity, and long time, which need to be improved.

Method used

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  • A Brain-Computer Interface Lead Optimization Method Based on Independent Component Analysis
  • A Brain-Computer Interface Lead Optimization Method Based on Independent Component Analysis
  • A Brain-Computer Interface Lead Optimization Method Based on Independent Component Analysis

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Embodiment Construction

[0049] 1. First define the following terms:

[0050] Single trial: single trial is an experimental time paradigm designed for single motor imagery EEG data collection, the trial duration is 10s, and the time interval between adjacent single trials is 2-3s

[0051] Single test sample: EEG data x collected in a single test, its data format is n×T matrix, n is the number of EEG leads, and T is the length of the EEG data sample collected in a single test.

[0052] Data set: the data set is an EEG signal data set including L single trial samples.

[0053] The training data set is the data set used in the high-quality training data selection stage; the test data set is the data set used in the optimal lead combination selection stage.

[0054] Initial lead combination and candidate lead combination: the initial lead combination is the lead combination close to the motor cortex of the brain; the electrodes used in the initial lead combination are initial electrodes, and the electrod...

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Abstract

The invention discloses a brain-computer interface lead optimization method based on independent component analysis. The method includes the following steps: building ICA based on a single test sampledesign ICA-MIBCI system for selecting high quality single test data from the EEG training set to ensure subsequent ICA-MIBCI system design reliability. On the basis, a new EEG lead optimization strategy is adopted to automatically optimize EEG leads for different BCI users. The invention is applied to ICA-MIBCI system can not only complete EEG lead selection based on specific person quickly and accurately, but also alleviate the impact of low-quality training data on MIBCI performance. At the same time, the advantages of ICA spatial filtering method in real EEG source acquisition and location, model migration and training data acquisition can be better utilized, and the stability and practicability of ICA-MIBCI system can be further effectively improved.

Description

technical field [0001] The invention relates to the technical field of brain-computer interface (BCI), in particular to a method for optimizing leads of the brain-computer interface based on independent component analysis. Background technique [0002] Brain-Computer Interface technology can provide a channel for direct communication with external devices for people with normal thinking functions but motor dysfunction, without relying on the normal output channels of the brain (peripheral nerves and muscles). Among them, Motor Imagery Brain-Computer Interface (MIBCI) based on motor imagery has been widely used. According to neurophysiological knowledge, when unilateral limb movement or motor imagery is performed, the mu rhythm (8-12Hz) and beta (18-26Hz) rhythm of the electroencephalogram (EEG) in the motor cortex area on the opposite side of the limb will be induced. The amplitude of the frequency spectrum oscillation decreases, which makes the spectrum of the μ rhythm and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F3/01G06K9/00G06K9/62
CPCG06F3/015G06F2203/011G06F2218/12G06F18/2134
Inventor 吴小培阮晶周蚌艳郭晓静张磊吕钊高湘萍
Owner ANHUI UNIVERSITY
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