Nakanishi Masaki, Wang Yu-Te, Jung Tzyy-Ping
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:89-92. doi: 10.1109/EMBC.2018.8512269.
Recent studies have shown that using the user's average steady-state visual evoked responses (SSVEPs) as the template to template-matching methods could significantly improve the accuracy and speed of the SSVEP-based brain- computer interface (BCI). However, collecting the pilot data for each individual can be time-consuming. To resolve this practical issue, this study aims to explore the feasibility of leveraging pre- recorded datasets from the same users by transferring common electroencephalogram (EEG) responses across different sessions with the same or different electrode montages. The proposed method employs spatial filtering techniques including response averaging, canonical correlation analysis (CCA), and task- related component analysis (TRCA) to project scalp EEG recordings onto a shared response domain. The transferability was evaluated by using 40-class SSVEPs recorded from eight subjects with nine electrodes on two different days. Three subsets of electrode montages were selected to simulate different scenarios such as identical, partly overlapped, and non-overlapped electrode placements across two sessions. The target identification accuracy of the proposed methods with transferred training data significantly outperformed a conventional training-free algorithm. The result suggests training data required in the BCI speller could be transferred from different EEG montages and/or headsets.
最近的研究表明，将用户的平均稳态视觉诱发电位（SSVEPs）用作模板匹配方法的模板，可以显著提高基于SSVEP的脑机接口（BCI）的准确性和速度。然而，为每个个体收集试验数据可能很耗时。为了解决这个实际问题，本研究旨在通过在具有相同或不同电极导联的不同会话之间传输常见的脑电图（EEG）响应，探索利用同一用户预先记录的数据集的可行性。所提出的方法采用空间滤波技术，包括响应平均、典型相关分析（CCA）和任务相关成分分析（TRCA），将头皮EEG记录投影到共享响应域。通过使用从八名受试者在两天内用九个电极记录的40类SSVEPs来评估可转移性。选择了三个电极导联子集来模拟不同场景，例如两个会话之间相同、部分重叠和不重叠的电极放置。所提出的方法在使用转移训练数据时的目标识别准确率显著优于传统的无训练算法。结果表明，BCI拼写器所需的训练数据可以从不同的EEG导联和/或耳机中转移。
