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Updated: Mar 2, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

ATCRN: Attention-guided Temporal Convolutional Remix Network for P300 speller.

Binghao Shi¹, Mengxue Liu¹, Yanjiang Wang¹

¹The College of Control Science and Engineering, China University of Petroleum(East China), Shandong Qingdao 266580, China.

Journal of Neuroscience Methods

|February 28, 2026

Summary

This study introduces the Attention-guided Temporal Convolutional Remix Network (ATCRN) to improve brain-computer interface (BCI) communication. The ATCRN model enhances P300 speller performance by effectively handling noisy EEG signals and variable P300 responses.

Keywords:

Attention mechanism Brain-computer interface (BCI)EEG decoding P300 speller Temporal convolutional network

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Brain-computer interfaces (BCIs) utilize P300 event-related potentials for communication.
P300 spellers face challenges due to low EEG signal-to-noise ratio and P300 temporal variability.

Purpose of the Study:

To develop an advanced decoding framework for P300 spellers.
To enhance the accuracy and efficiency of BCI communication systems.

Main Methods:

Proposed the Attention-guided Temporal Convolutional Remix Network (ATCRN), an end-to-end deep learning model.
Integrated a novel Temporal Convolutional Remix Network (TCRN) with multi-level skip connections for temporal feature fusion.
Employed dual-attention mechanisms (CBAM and ECA) for noise suppression and channel recalibration.

Main Results:

Achieved high character recognition rates (99% and 98%) on BCI Competition III Dataset II.
Demonstrated superior information transfer rates and robust P300 detection in ALS patients (average AUC-ROC 0.882).
Outperformed existing CNN, TCN, and Transformer-based models, achieving state-of-the-art results.

Conclusions:

The ATCRN offers a robust and effective decoding framework for P300 spellers.
The synergy of TCRN and dual-attention mechanisms addresses key limitations in BCI performance.
This approach presents a practical advancement for future BCI applications.