脑-机接口
计算机科学
概化理论
适应(眼睛)
校准
过程(计算)
会话(web分析)
接口(物质)
机器学习
人工智能
学习迁移
数据挖掘
脑电图
统计
心理学
物理
数学
气泡
最大气泡压力法
精神科
并行计算
万维网
光学
操作系统
作者
Nanlin Shi,Xiang Li,Bingchuan Liu,Chen Yang,Yijun Wang,Xiaorong Gao
出处
期刊:IEEE Transactions on Neural Systems and Rehabilitation Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:31: 1521-1531
被引量:8
标识
DOI:10.1109/tnsre.2023.3245654
摘要
The tradeoff between calibration effort and model performance still hinders the user experience for steady-state visual evoked brain-computer interfaces (SSVEP-BCI). To address this issue and improve model generalizability, this work investigated the adaptation from the cross-dataset model to avoid the training process, while maintaining high prediction ability.When a new subject enrolls, a group of user-independent (UI) models is recommended as the representative from a multi-source data pool. The representative model is then augmented with online adaptation and transfer learning techniques based on user-dependent (UD) data. The proposed method is validated on both offline (N=55) and online (N=12) experiments.Compared with the UD adaptation, the recommended representative model relieved approximately 160 trials of calibration efforts for a new user. In the online experiment, the time window decreased from 2 s to 0.56±0.2 s, while maintaining high prediction accuracy of 0.89-0.96. Finally, the proposed method achieved the average information transfer rate (ITR) of 243.49 bits/min, which is the highest ITR ever reported in a complete calibration-free setting. The results of the offline result were consistent with the online experiment.Representatives can be recommended even in a cross-subject/device/session situation. With the help of represented UI data, the proposed method can achieve sustained high performance without a training process.This work provides an adaptive approach to the transferable model for SSVEP-BCIs, enabling a more generalized, plug-and-play and high-performance BCI free of calibrations.
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