PDF(Pubmed)
Abstract：
Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or other external devices. They can extend a person\'s degree of freedom by either strengthening or substituting the human peripheral working capacity. Moreover, their potential clinical applications in medical fields include rehabilitation, affective computing, communication, and control. Over the last decade, noninvasive BCI systems such as electroencephalogram (EEG) have progressed from simple statistical models to deep learning models, with performance improvement over time and enhanced computational power. However, numerous challenges pertaining to the clinical use of BCI systems remain, e.g., the lack of sufficient data to learn more possible features for robust and reliable classification. However, compared with fields such as computer vision and speech recognition, the training samples in the medical BCI field are limited as they target patients who face difficulty generating EEG data compared with healthy control. Because deep learning models incorporate several parameters, they require considerably more data than other conventional methods. Thus, deep learning models have not been thoroughly leveraged in medical BCI. This study summarizes the state-of-the-art progress of the BCI system over the last decade, highlighting critical challenges and solutions.
摘要：
脑机接口(BCI)实现了大脑与计算机或其他外部设备之间的直接通信。它们可以通过加强或替代人的外围工作能力来扩展人的自由度。此外，它们在医学领域的潜在临床应用包括康复，情感计算，通信,和控制。在过去的十年里，诸如脑电图（EEG）之类的无创BCI系统已经从简单的统计模型发展到深度学习模型，随着时间的推移，性能提高，计算能力增强。然而,与BCI系统的临床使用有关的许多挑战仍然存在，例如，缺乏足够的数据来学习更多可能的特征以进行稳健和可靠的分类。然而,与计算机视觉和语音识别等领域相比，医学BCI领域的训练样本是有限的，因为它们针对的是与健康对照相比难以生成EEG数据的患者。因为深度学习模型包含几个参数，它们比其他传统方法需要更多的数据。因此，深度学习模型尚未在医学BCI中得到彻底利用。本研究总结了BCI系统在过去十年中的最新进展，强调关键挑战和解决方案。
