Enabling Communication for Locked-in Syndrome Patients using Deep Learning and an Emoji-based Brain Computer Interface
Enabling Communication for Locked-in Syndrome Patients using Deep Learning and an Emoji-based Brain Computer Interface
复制标题
使用深度学习和基于表情符号的脑机接口为闭锁综合症患者提供交流
DOI：
10.1109/biocas.2018.8584821
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
L. Najafizadeh
中科院分区：
文献类型：
--
作者：
A. Comaniciu;L. Najafizadeh
闭锁综合征描述了一种患者无法说话或移动的情况，尽管他们保留了认知能力。在本文中，我们提出了一种新的脑机接口设计，使用基于多功能表情符号的符号显示和深度学习解决方案，使这些患者能够使用通过脑电图（EEG）获得的记录进行交流。EEG信号被转换成代表其时空特征的图像。然后使用深度卷积神经网络（CNN）对图像进行分类，以识别预期的表情符号。该系统的原型进行了测试，对5名健康志愿者，显示出显着的提高识别率相比，经典的LDA分类。
Locked-in syndrome describes a condition in which patients are incapable of speaking or moving, although they do retain their cognitive capabilities. In this paper, we propose a novel Brain Computer Interface design using a versatile emoji-based symbol display and a deep learning solution to enable these patients to communicate using recordings obtained through electroencephalography (EEG). EEG signals are converted into images representing their spatiotemporal characteristics. Images are then classified using a deep convolutional neural network (CNN) to recognize the intended emoji symbol. A prototype of the proposed system was tested on five healthy volunteers, showing significant improvement in the recognition rate when compared to the classic LDA classifier.