Sung Dong-Jin, Kim Keun-Tae, Jeong Ji-Hyeok, Kim Laehyun, Lee Song Joo, Kim Hyungmin, Kim Seung-Jong
Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
Department of Biomedical Engineering, Korea University College of Medicine, Seoul, 02841, Republic of Korea.
Heliyon. 2024 Sep 3;10(17):e37343. doi: 10.1016/j.heliyon.2024.e37343. eCollection 2024 Sep 15.
Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography (EEG) have found practical applications in external device control. However, the non-stationary nature of EEG signals remains to obstruct BCI performance across multiple sessions, even for the same user. In this study, we aim to address the impact of non-stationarity, also known as inter-session variability, on multi-session MI classification performance by introducing a novel approach, the relevant session-transfer (RST) method. Leveraging the cosine similarity as a benchmark, the RST method transfers relevant EEG data from the previous session to the current one. The effectiveness of the proposed RST method was investigated through performance comparisons with the self-calibrating method, which uses only the data from the current session, and the whole-session transfer method, which utilizes data from all prior sessions. We validated the effectiveness of these methods using two datasets: a large MI public dataset (Shu Dataset) and our own dataset of gait-related MI, which includes both healthy participants and individuals with spinal cord injuries. Our experimental results revealed that the proposed RST method leads to a 2.29 % improvement (p < 0.001) in the Shu Dataset and up to a 6.37 % improvement in our dataset when compared to the self-calibrating method. Moreover, our method surpassed the performance of the recent highest-performing method that utilized the Shu Dataset, providing further support for the efficacy of the RST method in improving multi-session MI classification performance. Consequently, our findings confirm that the proposed RST method can improve classification performance across multiple sessions in practical MI-BCIs.
基于运动想象（MI）的脑机接口（BCI）利用脑电图（EEG）已在外部设备控制中得到实际应用。然而，EEG信号的非平稳特性仍然阻碍着跨多个会话的BCI性能，即使对于同一用户也是如此。在本研究中，我们旨在通过引入一种新颖的方法——相关会话转移（RST）方法，来解决非平稳性（也称为会话间变异性）对多会话MI分类性能的影响。RST方法以余弦相似度为基准，将前一会话的相关EEG数据转移到当前会话。通过与仅使用当前会话数据的自校准方法以及利用所有先前会话数据的全会话转移方法进行性能比较，研究了所提出的RST方法的有效性。我们使用两个数据集验证了这些方法的有效性：一个大型MI公共数据集（舒数据集）和我们自己的步态相关MI数据集，其中包括健康参与者和脊髓损伤患者。我们的实验结果表明，与自校准方法相比，所提出的RST方法在舒数据集中的性能提高了2.29%（p<0.001），在我们的数据集中提高了高达6.37%。此外，我们的方法超过了利用舒数据集的近期最高性能方法的性能，为RST方法在提高多会话MI分类性能方面的有效性提供了进一步支持。因此，我们的研究结果证实，所提出的RST方法可以提高实际MI-BCI中跨多个会话的分类性能。
