用脑电波实现个性化脑机接口，识别用户与意图
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms
- 基于运动、言语和视觉想象的脑电信号构建个性化框架
- 用户识别准确率达99.5%，运动想象意图识别效果最佳
- 适合需要高精度身份认证与意图理解的脑机应用
本文提出一种基于内源性脑电图（EEG）范式的个性化脑机接口（BCI）应用概念框架，通过运动想象（MI）、言语想象（SI）和视觉想象（VI）等范式，实现服务定制化。框架包含用户识别与意图分类两个核心模块，利用EEG信号实现个体身份确认与动作意图解码。基于8名受试者采集的私有数据集，采用ShallowConvNet模型进行特征解码。实验结果表明，用户识别平均准确率达0.995，意图分类整体准确率为0.47，其中运动想象表现最优。结果表明，脑电信号可有效支撑个性化BCI应用，尤其在运动与言语想象任务中具有强鲁棒性与可靠性。
原文摘要 · Abstract (English)
In this paper, we propose a conceptual framework for personalized brain-computer interface (BCI) applications, which can offer an enhanced user experience by customizing services to individual preferences and needs, based on endogenous electroencephalography (EEG) paradigms including motor imagery (MI), speech imagery (SI), and visual imagery. The framework includes two essential components: user identification and intention classification, which enable personalized services by identifying individual users and recognizing their intended actions through EEG signals. We validate the feasibility of our framework using a private EEG dataset collected from eight subjects, employing the ShallowConvNet architecture to decode EEG features. The experimental results demonstrate that user identification achieved an average classification accuracy of 0.995, while intention classification achieved 0.47 accuracy across all paradigms, with MI demonstrating the best performance. These findings indicate that EEG signals can effectively support personalized BCI applications, offering robust identification and reliable intention decoding, especially for MI and SI.
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