结合 xDAWN 空间滤波与滤波器组卷积的紧凑网络用于 P300 检测
A Compact xDAWN-Guided Spatio-Temporal Network with Filter-Bank Convolution for P300 Detection
AI 导读
研究者提出一种仅含 5,100 个可训练参数的紧凑深度网络，用 xDAWN 空间滤波、固定频率滤波器组与深度可分离卷积解码 P300 脑电信号。在 BCI Competition III 数据集上两次重复刺激达到 17.8 bits/min，BCI Competition II 上达到 40.5 bits/min，兼顾准确率与低计算成本。
正文 · 原文
Abstract:P300-based brain-computer interfaces (BCIs) enable communication by decoding stimulus-evoked EEG responses into character selections without requiring voluntary motor output. Reliable P300 detection remains difficult because of low response amplitude and signal-to-noise ratio, while practical spellers require accurate character selection with few stimulus repetitions and low computational cost. We propose a compact deep learning architecture that combines xDAWN spatial filtering for event-related potential enhancement and dimensionality reduction, a fixed frequency-selective filter bank, and convolutional feature learning for spatio-temporal EEG decoding. Depthwise-separable convolutions and progressive average pooling reduce computational complexity while retaining discriminative temporal information. The resulting target/non-target classifier contains only 5,100 trainable parameters. We evaluated the model on two public benchmarks, BCI Competition II Dataset IIb and BCI Competition III Dataset II, and compared it with established P300 decoding methods across different numbers of stimulus repetitions. The model achieved an information transfer rate of 17.8 bits/min with two repetitions on BCI Competition III and 40.5 bits/min on BCI Competition II, demonstrating a favorable accuracy-speed trade-off. These results suggest that combining domain-informed spatial and spectral priors with compact neural networks can support efficient P300 decoding for EEG-based BCI communication.
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.17101 [cs.HC] |
| (or arXiv:2611.17101v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.17101 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mohammad Mehdi Habibi Bina [view email]
[v1]
Thu, 12 Nov 2026 17:00:30 UTC (939 KB)
来源：arXiv cs.AI · arxiv.org