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.

翻译：本文提出了一种个性化脑机接口（BCI）应用的概念框架。该框架基于运动想象（MI）、言语想象（SI）和视觉想象等内源性脑电图（EEG）范式，通过根据个体偏好和需求定制服务，以提供增强的用户体验。该框架包含两个核心组件：用户识别与意图分类，它们通过脑电信号识别个体用户并解析其意图动作，从而实现个性化服务。我们利用从八名受试者采集的私有脑电数据集，采用ShallowConvNet架构解码脑电特征，验证了该框架的可行性。实验结果表明，用户识别的平均分类准确率达到0.995，而所有范式下的意图分类准确率为0.47，其中MI范式表现最佳。这些发现表明，脑电信号能够有效支持个性化BCI应用，提供鲁棒的身份识别与可靠的意图解码，尤其在MI和SI范式中表现突出。