Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN
Open accessAbstract
Introduction: Currently, it is still a challenge to detect single-trial P300 from electroencephalography (EEG) signals. In this paper, to address the typical problems faced by existing single-trial P300 classification, such as complex, time-consuming and low accuracy processes, a single-trial P300 classification algorithm based on multiplayer data fusion convolutional neural network (CNN) is proposed to construct a centralized collaborative brain-computer interfaces (cBCI) for fast and highly accurate classification of P300 EEG signals. Methods: In this paper, two multi-person data fusion methods (parallel data fusion and serial data fusion) are used in the data pre-processing stage to fuse multi-person EEG information stimulated by the same task instructions, and then the fused data is fed as input to the CNN for classification. In building the CNN network for single-trial P300 classification, the Conv layer was first used to extract the features of single-trial P300, and then the Maxpooling layer was used to connect the Flatten layer for secondary feature extraction and dimensionality reduction, thereby simplifying the computation. Finally batch normalisation is used to train small batches of data in order to better generalize the network and speed up single-trial P300 signal classification. Results: In this paper, the above new algorithms were tested on the Kaggle dataset and the Brain-Computer Interface (BCI) Competition III dataset, and by analyzing the P300 waveform features and EEG topography and the four standard evaluation metrics, namely Accuracy, Precision, Recall and F1-score,it was demonstrated that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed other classification algorithms. Discussion: The results show that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed the single-person model, and that the single-trial P300 classification algorithm with two multi-person data fusion CNNs involves smaller models, fewer training parameters, higher classification accuracy and improves the overall P300-cBCI classification rate and actual performance more effectively with a small amount of sample information compared to other algorithms.
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Averaging multiple trials of EEG collections is time-consuming and inefficient.
“However, this averaging method is time-consuming and inefficient [14].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [14]
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- Authors
- Du, P., Li, P., Cheng, L., Li, X., & Su, J.
- Affiliations
- Tianjin University of Technology (CN)
- Corresponding author
- Li, Penghai
- Cheng, Longlong
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2023-02-22
- Volume
- 17
- Pages
- 1132290-1132290
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC9992797
- OpenAlex
- W4321497298
- Cited by
- 17
- References
- 33
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 9 · 2023: 2
- FWCI
- 2.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (19JCTPJC56000, 18JCYBJC95400, 61806146)
- Natural Science Foundation of Tianjin City (19JCTPJC56000, 61806146, 18JCYBJC95400)
- National Key Research and Development Program of China (2021YFF1200600)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- convolutional neural networks, EEG classification, EEG topography, small sample classification
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