The invention discloses a driving electroencephalogram signal recognition method based on a multi-domain asymptotic convolutional neural network, and relates to the field of electroencephalogram intelligent recognition. The method mainly comprises the steps of electroencephalogram (EEG) acquisition, frequency domain feature and spatial feature extraction, and variable asymptotic convolutional neural network modeling, recognition and classification. After the collected EEG signals are preprocessed, frequency domain features are obtained through fast Fourier transform (FFT) processing, and spatial features are obtained through common spatial pattern (CSP) processing. All frequency band features are stacked, an input matrix is constructed and input to the constructed variable asymptotic convolutional neural network model, strategies used in different stages are different, and recognition classification about five driving behaviors is obtained. According to the method, the frequency domain-space features and the variable asymptotic convolutional neural network model are combined, and the global features of the EEG signals are more concerned, so that the model classification capability is remarkably improved, and the EEG signal classification and recognition effect is further improved. Meanwhile, the characteristic of light weight of the model meets the requirement of intelligent driving on real-time performance.

    本发明公开了一种基于多域渐近卷积神经网络的驾驶脑电信号识别方法,涉及到了脑电智能识别领域。主要由脑电信号(EEG)采集,频域特征、空间特征提取,可变式渐近卷积神经网络建模识别分类组成。对采集的EEG信号进行预处理后,通过快速傅里叶变换(FFT)处理得到频域特征,再通过共空间模式(CSP)处理得到空间特征。堆叠所有频带特征,构造输入矩阵,输入到所构造的可变式渐近卷积神经网络模型,不同阶段所使用的策略不同,得到关于五种驾驶行为的识别分类。本发明通过对频域‑空间特征和可变式渐近卷积神经网络模型结合,更加关注EEG信号的全局特征,从而显著提升了模型分类能力,进一步提升脑电信号分类识别的效果。同时,该模型轻量化的特点满足智能驾驶对实时性的要求。


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    Title :

    Driving electroencephalogram signal recognition method based on multi-domain asymptotic convolutional neural network


    Additional title:

    基于多域渐近卷积神经网络的驾驶脑电信号识别方法


    Contributors:
    CHEN HAIYI (author) / ZHANG XUEJUN (author) / LIU JINTAO (author) / LI TIANHONG (author)

    Publication date :

    2025-06-24


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung



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