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Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN.

Pu Du¹, Penghai Li¹, Longlong Cheng^1,2

¹School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, China.

Frontiers in Neuroscience

|March 13, 2023

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Detecting single-trial P300 signals from electroencephalography (EEG) is challenging. This study introduces a multiplayer data fusion convolutional neural network (CNN) for fast, accurate P300 classification in centralized collaborative brain-computer interfaces (cBCI).

Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Single-trial P300 detection from electroencephalography (EEG) signals presents significant challenges.
Existing methods for P300 classification are often complex, time-consuming, and yield low accuracy.

Purpose of the Study:

To develop a novel algorithm for fast and highly accurate single-trial P300 classification.
To construct a centralized collaborative brain-computer interface (cBCI) using this algorithm.

Main Methods:

Proposed a multiplayer data fusion convolutional neural network (CNN) for P300 classification.
Employed parallel and serial data fusion techniques to integrate multi-person EEG data.
Utilized Conv layers for feature extraction, Maxpooling for dimensionality reduction, and batch normalization for improved generalization and speed.

Keywords:

P300 classification centralized collaborative BCI convolutional neural networks multi-person data fusion single-trial

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Main Results:

The proposed algorithm was evaluated on Kaggle and BCI Competition III datasets.
Demonstrated significantly superior performance compared to other classification algorithms and single-person models.
Achieved higher classification accuracy with smaller models and fewer training parameters.

Conclusions:

The multiplayer data fusion CNN algorithm effectively enhances P300-cBCI classification rates and performance.
This approach offers a more efficient solution for single-trial P300 signal classification, even with limited sample data.