Nam Hyeonyeong, Kim Jun-Mo, Choi WooHyeok, Bak Soyeon, Kam Tae-Eui
Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.
Front Hum Neurosci. 2023 Jun 5;17:1205881. doi: 10.3389/fnhum.2023.1205881. eCollection 2023.
The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI.
In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario.
The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.
脑机接口（BCI）使个体能够利用其神经信号控制外部设备。一种流行的BCI范式是运动想象（MI），它涉及想象运动以诱发神经信号，这些信号可被解码以根据用户意图控制设备。由于脑电图（EEG）具有非侵入性和高时间分辨率，因此在运动想象脑机接口（MI-BCI）领域中经常用于从大脑获取神经信号。然而，EEG信号会受到噪声和伪迹的影响，并且EEG信号模式在不同受试者之间有所不同。因此，选择最具信息性的特征是提高MI-BCI分类性能的关键步骤之一。
在本研究中，我们设计了一种基于逐层相关传播（LRP）的特征选择方法，该方法可以轻松集成到基于深度学习（DL）的模型中。我们在依赖受试者的场景中，使用各种基于DL的主干模型，在两个不同的公开可用EEG数据集上评估其对可靠的类别判别EEG特征选择的有效性。
结果表明，基于LRP的特征选择提高了所有基于DL的主干模型在两个数据集上的MI分类性能。基于我们的分析，我们认为它可以将其能力扩展到不同的研究领域。
