AABAC: Adaptive Asynchronous Brain-Actuated Control

本项目旨在开发一种新型的自适应异步脑机接口（BCI）系统，用于智能系统和机器人的脑驱动控制。科学技术的最新进展揭示了将人类大脑与智能机器融合的可能性，以执行最先进的自主机器无法承担的挑战性任务。BCI是实现这一目标的关键技术之一。BCI系统检测和分析脑电波，例如，在一些实施例中，用户可以使用脑电图（EEG）信号，以便理解用户的精神状态，然后将精神状态转换为用于与计算机、机器人和其他系统通信并控制计算机、机器人和其他系统的命令。目前，几乎所有基于脑电的高精度脑机接口系统都使用同步协议，只能识别两种心理状态。它们的缺点包括低的信息传输速率和不自然的用户界面，这给BCI系统的实际应用带来了严重的限制。基于我们以前在BCI及相关领域的研究，我们认为，现在是非常及时的开发自适应和异步BCI系统，不仅具有使用异步协议的优点，如高的信息传输速率和自然的操作模式，但也受益于自适应学习，以提高系统的准确性和鲁棒性。除了自适应学习，为了实现高精度和鲁棒性，该计划将研究新的有效指标的发病检测和异步精神状态分类的最佳时机计划，发现或发明新的特征空间，它会更容易分类EEG模式，并开发新的方法，增加控制命令的数量映射到有限数量的精神状态。在此开发的方法将通过广泛的实验进行评估，实时脑驱动控制的智能轮椅和机器人手臂。

This proposed project aims to develop a novel adaptive and asynchronous brain-computer interface (BCI) system for brain-actuated control of intelligent systems and robots. Recent advances in science and technology have shed light on the possibility of fusing human's brain with intelligent machines to carry out challenging tasks that the state of the art autonomous machines cannot undertake. BCI is one of the key technologies to make this possible. A BCI system detects and analyses brain waves, e.g., electroencephalography (EEG) signals, in order to understand a user's mental states, and then translates the mental states into commands for communicating with and controlling computers, robots, and other systems. Almost all the current EEG-based BCI systems of high accuracy use synchronous protocols and recognise two mental states only. Their disadvantages include low information transfer rate and unnatural user interface, which impose severe limitations on BCI systems for real-world applications. Based on our previous research in BCI and related areas, we believe that it is now very timely to develop adaptive and asynchronous BCI systems that not only have the advantages of using asynchronous protocols, such as high information transfer rate and natural operation mode, but also benefit from adaptive learning so as to improve the system's accuracy and robustness. Apart from adaptive learning, in order to achieve high accuracy and robustness, this proposed programme will investigate novel effective indicators for onset detection and optimal timing schemes for asynchronous mental state classification, discover or invent new feature spaces on which it would be easier to classify EEG patterns, and develop new methods for increasing the number of control commands mapped from a limited number of mental states. The methods developed hereby will be assessed through extensive experimentation with real-time brain-actuated control of an intelligent wheelchair and a robotic arm.