AABAC: Adaptive Asynchronous Brain-Actuated Control

AABAC：自适应异步脑驱动控制

• 批准号: EP/D030099/1
• 负责人: Stephen Roberts
• 金额: $ 22.99万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD030099%2F1
• 关键词: AABAC; Adaptive; Asynchronous; Brain; Actuated

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.

本项目旨在开发一种新的自适应和异步脑机接口（BCI）系统，用于智能系统和机器人的脑驱动控制。最近科学技术的进步揭示了人类大脑与智能机器融合的可能性，以完成最先进的自主机器无法完成的具有挑战性的任务。BCI是实现这一目标的关键技术之一。脑机接口系统检测和分析脑电波，例如脑电图（EEG）信号，以了解用户的精神状态，然后将精神状态转换为与计算机、机器人和其他系统通信和控制的命令。目前几乎所有基于脑电图的高精度脑机接口系统都采用同步协议，只能识别两种精神状态。它们的缺点包括低信息传输速率和不自然的用户界面，这给BCI系统在实际应用中施加了严重的限制。基于我们之前在脑机接口及相关领域的研究，我们认为现在开发自适应和异步脑机接口系统是非常及时的，它不仅具有使用异步协议的优势，如高的信息传输速率和自然的运行方式，而且还受益于自适应学习，从而提高系统的准确性和鲁棒性。除了自适应学习之外，为了达到较高的准确性和鲁棒性，本项目还将研究新的有效的起病检测指标和异步精神状态分类的最佳时序方案，发现或发明新的特征空间，使脑电图模式更容易分类，并开发新的方法来增加从有限数量的精神状态映射的控制命令的数量。本文开发的方法将通过对智能轮椅和机械臂的实时脑动控制进行广泛的实验来评估。

项目成果

Fault Tolerant Control Using Gaussian Processes and Model Predictive Control

• DOI: 10.1515/amcs-2015-0010
• 发表时间: 2013-10
• 期刊: International Journal of Applied Mathematics and Computer Science
• 影响因子: 1.9
• 作者: Xiaoke Yang;J. Maciejowski

Bayesian inference for an adaptive Ordered Probit model: an application to Brain Computer Interfacing.

自适应有序概率模型的贝叶斯推理：脑机接口的应用。

• DOI: 10.1016/j.neunet.2011.03.019
• 发表时间: 2011
• 期刊: the official journal of the International Neural Network Society
• 作者: Yoon JW

An adaptive, sparse-feedback EEG classifier for self-paced BCI.

用于自定进度 BCI 的自适应稀疏反馈 EEG 分类器。

• 发表时间: 2006
• 作者: S Roberts