Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms

TL;DR

This paper presents a framework for personalized BCI applications using endogenous EEG paradigms, focusing on user identification and intention decoding to enhance user experience.

Contribution

It introduces a novel conceptual framework for personalized BCI based on endogenous EEG signals, validated with a new dataset and deep learning methods.

Findings

User identification accuracy of 0.995

Intention classification accuracy of 0.47

Motor imagery showed the best performance

Abstract

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.