Boosting template-based SSVEP decoding by cross-domain transfer learning
Boosting template-based SSVEP decoding by cross-domain transfer learning
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DOI：
10.1088/1741-2552/abcb6e
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发表时间：
2020-11
影响因子：
4
通讯作者：
Kuan-Jung Chiang;Chun-Shu Wei;M. Nakanishi;T. Jung
中科院分区：
文献类型：
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作者：
Kuan-Jung Chiang;Chun-Shu Wei;M. Nakanishi;T. Jung
Objective.本研究旨在建立一个通用的迁移学习框架，通过利用跨域数据传输来提高基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）的性能。Approach.我们通过结合基于最小二乘变换（LST）的迁移学习来利用多个领域（会话、受试者和脑电图蒙太奇）的校准数据，增强了最先进的基于模板的SSVEP解码。主要结果。研究结果验证了LST在跨域传输现有数据时消除SSVEP变化的有效性。此外，基于LST的方法比基于标准任务相关成分分析（TRCA）的方法和非LST朴素迁移学习方法实现了显着更高的SSVEP解码准确性。意义本研究证明了基于LST的迁移学习能够利用受试者和/或器械的现有数据，并对其在各种情况下的原理和行为进行了深入研究。当校准数据有限时，所提出的框架显着提高了SSVEP解码精度。它的性能在校准减少可以促进即插即用SSVEP为基础的BCI和进一步的实际应用。
Objective. This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) by leveraging cross-domain data transferring. Approach. We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and electroencephalogram montages). Main results. Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method. Significance. This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.