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Single-trial classification of event-related potentials in rapid serial visual presentation tasks using supervised

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    IEEE Transactions on Neural Networks and Learning Systems
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    Detecting single-trial event-related potentials (ERPs) in electroencephalogram (EEG) is improved using a novel convolutional neural network (CNN) with spatial filtering. This approach enhances ERP detection accuracy by optimizing spatial filtering and classification together.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Machine Learning

    Background:

    • Accurate single-trial event-related potential (ERP) detection in electroencephalogram (EEG) is challenging.
    • Supervised spatial filtering enhances discriminative information in EEG data for improved ERP detection.

    Purpose of the Study:

    • To propose a novel convolutional neural network (CNN) with a dedicated spatial filtering layer for ERP detection.
    • To evaluate the CNN's performance against traditional classifiers using AUC maximization for training.

    Main Methods:

    • A CNN with a spatial filtering layer was developed for ERP detection.
    • The CNN was compared with Bayesian linear discriminant analysis, multilayer perceptron (MLP), and support vector machines.
    • Data were pre-processed using xDAWN, common spatial pattern, or no spatial filtering, with training optimized by AUC maximization.

    Main Results:

    • Classification performance for target discrimination depended on the interplay between spatial filtering and classifier choice.
    • The nonlinear MLP classifier demonstrated superior performance compared to linear methods.
    • Training based on AUC maximization yielded better results than minimizing mean square error.

    Conclusions:

    • The selection of system architecture, encompassing both spatial filtering and classification, is critical for effective ERP detection.
    • Integrating spatial filtering within a CNN framework offers a promising approach for enhancing single-trial ERP analysis.