Performance improvement of Ear-EEG SSVEP-BCI using reliability score

IF 0.8 Q4 ROBOTICS

Artificial Life and Robotics Pub Date : 2025-04-30 DOI:10.1007/s10015-025-01025-1

Sodai Kondo, Hideyuki Harafuji, Ren Kiuchi, Asahi Saito, Kakeru Tanaka, Wataru Wakayama, Hisaya Tanaka

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引用次数: 0

Abstract

Steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) are known for high speed, accuracy, and multivalue input. Integrating ear-electroencephalogram (EEG) can make SSVEP-BCI more accessible for everyday use. This study introduces a reliability score to enhance the performance of ear-EEG SSVEP-BCI by dynamically adjusting measurement duration and enabling asynchronous detection. Two analysis methods, learning canonical correlation analysis (LCCA) and task-related component analysis, were evaluated. Using the reliability score, the accuracy for ear-EEG SSVEP-BCI reached \(100\)% with an information transfer rate (ITR) of \(22.36\pm 3.54\) bits/min, compared to \(61.93\pm 9.22\)% accuracy and \(15.32\pm 4.59\) bits/min ITR without the reliability score. These findings demonstrate that the reliability score significantly improves ear-EEG SSVEP-BCI performance, suggesting its potential to enhance usability in practical applications.

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采用信度评分法改进耳- eeg SSVEP-BCI的性能

稳态视觉诱发电位（SSVEP）脑机接口（BCI）以高速、准确和多值输入而闻名。整合耳脑电图（EEG）可以使SSVEP-BCI更易于日常使用。本研究引入信度评分，通过动态调整测量时间和实现异步检测来提高耳-脑SSVEP-BCI的性能。评估了学习典型相关分析（LCCA）和任务相关成分分析（task-related component analysis）两种分析方法。采用信度评分，耳-脑SSVEP-BCI的准确率达到 \(100\)% with an information transfer rate (ITR) of \(22.36\pm 3.54\) bits/min, compared to \(61.93\pm 9.22\)% accuracy and \(15.32\pm 4.59\) bits/min ITR without the reliability score. These findings demonstrate that the reliability score significantly improves ear-EEG SSVEP-BCI performance, suggesting its potential to enhance usability in practical applications.

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来源期刊

Artificial Life and Robotics ROBOTICS-

CiteScore

2.00

自引率

22.20%

发文量

101

期刊介绍： Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the twenty-first century. This journal covers a broad multidisciplinary field, including areas such as artificial brain research, artificial intelligence, artificial life, artificial living, artificial mind research, brain science, chaos, cognitive science, complexity, computer graphics, evolutionary computations, fuzzy control, genetic algorithms, innovative computations, intelligent control and modelling, micromachines, micro-robot world cup soccer tournament, mobile vehicles, neural networks, neurocomputers, neurocomputing technologies and applications, robotics, robus virtual engineering, and virtual reality. Hardware-oriented submissions are particularly welcome. Publishing body: International Symposium on Artificial Life and RoboticsEditor-in-Chiei: Hiroshi Tanaka Hatanaka R Apartment 101, Hatanaka 8-7A, Ooaza-Hatanaka, Oita city, Oita, Japan 870-0856 ©International Symposium on Artificial Life and Robotics