Method for detecting spatial selective attention on basis of grey theories

A technology of grey theory and detection method, applied in cognitive research and selective attention field, to achieve the effect of high recognition accuracy and high resolution

Active Publication Date: 2017-01-25
NORTHWESTERN POLYTECHNICAL UNIV
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Therefore, the analysis and processing of EEG

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  • Method for detecting spatial selective attention on basis of grey theories
  • Method for detecting spatial selective attention on basis of grey theories
  • Method for detecting spatial selective attention on basis of grey theories

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Experimental program
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Embodiment Construction

[0027] specific implementation plan

[0028] 1. Build as figure 2 In the visual stimulation system shown, the frequencies of stimulation signals at different locations in space are different, so that different frequencies represent different locations. The stimulation source adopts LED driven by FPGA, which has high stimulation intensity, frequency accuracy and adjustability;

[0029] 2. Carry out attention test experiments on this system and read EEG signals at the same time. Perform SSVEP detection and processing.

[0030] SSVEP is evoked EEG and is characterized by an increase in the amplitude of the EEG component corresponding to the stimulus frequency when focusing on flickering stimuli. Therefore, the present invention proposes a gray relational analysis method to extract features from the SSVEP signal.

[0031] Because the characteristics of the SSVEP signal in the frequency domain are particularly obvious, it is first considered to transfer the time domain signal ...

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Abstract

The invention provides a method for analyzing spatial selective attention on the basis of grey theories, relates to the field of cognition research, and particularly relates to the field of selective attention. The method includes 1, establishing vision stimulation systems; 2, carrying out attention testing experiments on the systems and reading brain electric signals; 3, carrying out Fourier transformation on the read brain electric signals; 4, generating a group of reference signals (sinusoidal signals with the frequencies equal to stimulation flicker frequencies) and solving Fourier transformation on the reference signals; 5, carrying out grey correlation on the transformed brain electric signals and the transformed reference signals and solving grey correlation coefficients of the brain electric signals and the reference signals; 6, classifying correlation vectors by the aid of support vector machine classifiers to obtain spatial concentration locations of the attention. The correlation vectors comprise the grey correlation coefficients. The method has the advantages that as shown by repeated measurement, the detection speed can be increased by algorithms, the detection precision can be improved by the algorithms, and accordingly tested spatial selective attention modes and states can be possibly monitored in real time.

Description

technical field [0001] The invention relates to the field of cognitive research, in particular to the field of selective attention, in particular to a gray theory-based spatial selective attention detection method. Background technique [0002] 80% of human information is obtained through visual channels, and vision is an important way to obtain information. Therefore, the study of visual information processing is a direct means to understand the information processing and advanced functional mechanism of the human brain. In the 1980s, scientific researchers began to pay attention to the relationship between attention and the allocation of brain processing resources. Because the computing resources of the brain are limited, at the same time, humans can only process limited resources. Faced with many visual stimulus inputs at the same time, the human neuro-visual system will selectively pay attention to some spatial orientations and ignore other stimuli, that is, visuospati...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 谢松云刘畅段绪吴悠侯文刚
Owner NORTHWESTERN POLYTECHNICAL UNIV
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