Alternate Modes of Human-Computer Interaction: EEG Recognition with Neural Networks

这是一项持续三年努力的第一年。本研究旨在探讨利用脑电图（EEG）模式的在线识别来进行人机交互的可行性。一个可以在线识别一个人正在进行的脑力活动的系统可以提供一个字母表，一个严重残疾的人可以用它来编写对轮椅等设备的命令。大多数先前的研究都侧重于识别的准确性，而不是快速、实时的反应。本研究的目的是分析当前脑电图识别方法的局限性，利用神经网络方法提高脑电图识别的准确性，减少脑电图识别的计算时间，并开发一种实用的实时方法。本文将比较传统方法和针对高维数据设计的神经网络学习方法的性能。特征和分类器特征的评估将基于分类精度、鲁棒性和计算速度。这个项目与模式识别领域有关，特别是与神经网络和人机交互领域有关。

This is the first year of a three year continuing effort. This research is to study the feasibility of human-computer interaction through the use of on-line recognition of electroencephalogram (EEG) patterns. A system that can identify, on-line, which of several mental tasks a person is performing could provide an alphabet with which a severely disabled person can compose commands to devices like a wheel chair. Most prior research has focused on recognition accuracy rather than on fast, real-time responses. The goals of this research are to characterize the limitations of current EEG recognition methods, to increase the accuracy and decrease the computation time of EEG recognition by use of a neural network approach, and to develop a practical real-time method. The performance of conventional methods and of neural network learning methods designed for high- dimensional data will be compared. Evaluations of feature and classifier characteristics will be based on classification accuracy, robustness and computation speed. This project has relevance to the field of pattern recognition in general, and to the neural network and human-computer interaction fields in particular.