MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
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DOI：
10.1109/tbme.2021.3137184
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发表时间：
2022-06-01
影响因子：
4.6
通讯作者：
Wilaiprasitporn, Theerawit
中科院分区：
文献类型：
--
作者：
Autthasan, Phairot;Chaisaen, Rattanaphon;Wilaiprasitporn, Theerawit
目的：基于运动想象（MI）的脑机接口（BCI）的进展允许通过解码神经生理现象来控制几种应用，这些现象通常由脑电图（EEG）使用非侵入性技术记录。尽管基于MI的BCI取得了重大进展，但EEG节律对受试者是特定的，并且随着时间的推移会发生各种变化。这些问题指出了提高分类性能的重大挑战，特别是以独立于主题的方式。方法：为了克服这些挑战，我们提出了MIN 2Net，一种新的端到端多任务学习来解决这个任务。我们将深度度量学习集成到多任务自动编码器中，以从EEG中学习紧凑且有区别的潜在表示，并同时执行分类。结果：该方法降低了预处理的复杂度，显著提高了脑电分类的性能。以独立于受试者的方式进行的实验结果表明，MIN 2Net的性能优于最先进的技术，在SMR-BCI和OpenBMI数据集上分别实现了6.72%和2.23%的F1分数提高。结论：我们证明MIN 2Net提高了潜在表征中的区分信息。意义：这项研究表明，使用此模型开发基于MI的BCI应用程序的新用户无需校准的可能性和实用性。
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. Methods: To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. Results: This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively. Conclusion: We demonstrate that MIN2Net improves discriminative information in the latent representation. Significance: This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.