Computer and Information Science Department, University of Michigan-Dearborn, 4901 Evergreen Rd., CIS 112, Dearborn, MI, USA.
Biomed Res Int. 2018 Feb 5;2018:9796238. doi: 10.1155/2018/9796238. eCollection 2018.
A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. We evaluate subject-specific models against other subjects. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care.
重症监护病房（ICU）患者面临的主要困境是沟通手段不一致且效率低下。患者对大多数沟通环节的评价都很困难且不成功。这反过来又会导致痛苦、未被识别的疼痛、焦虑和恐惧。因此，我们设计了一种用于 ICU 通信的便携式脑机接口系统（BCI4ICU），该系统经过优化，可在 ICU 环境中有效运行。该系统使用可穿戴式 EEG 帽和设计在移动设备上的 Android 应用程序，作为视觉刺激和数据处理模块。此外，为了克服 BCI 系统在实际场景中面临的挑战，我们提出了一种新颖的基于特定于主题的高斯混合模型（GMM）的训练和自适应算法。首先，我们使用基于 GMM 的训练和自适应在 SSVEP 识别模型的训练阶段中纳入特定于主题的信息。我们针对其他主题评估特定于主题的模型。随后，从 GMM 判别分数中，我们生成变换向量，将其传递到我们的预测模型。最后，利用特定于主题的 GMM 的自适应混合均值分数来生成高维超向量。我们的实验结果表明，所提出的系统实现了 98.7%的平均识别准确率，这对于为重症患者提供有效和一致的沟通是很有前景的。
