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.

该提出的项目旨在开发一种新型的自适应和异步脑机构界面（BCI）系统，以控制智能系统和机器人的脑部驱动。科学技术的最新进展揭示了将人的大脑与智能机器融合在一起的可能性，以执行最具挑战性的任务，而自动机器状态无法执行。 BCI是实现这一目标的关键技术之一。 BCI系统检测和分析脑波，例如脑电图（EEG）信号，以了解用户的精神状态，然后将精神状态转化为与计算机，机器人和其他系统进行通信的命令。几乎所有基于EEG的BCI高精度的BCI系统都使用同步协议，仅认识两个精神状态。他们的缺点包括低信息传输率和非自然用户界面，这对现实世界应用的BCI系统施加了严重的限制。根据我们以前在BCI及相关领域的研究，我们认为现在非常及时地开发适应性和同步的BCI系统，这些系统不仅具有使用异步方案的优势，例如高信息传递率和自然操作模式，而且还受益于适应性学习，以提高系统的准确性和鲁棒性。除自适应学习外，为了实现高准确性和鲁棒性，该提议的计划还将调查用于发作检测和最佳时机分类的新颖有效指标，以进行异步心理状态分类，发现或发明新功能空间，在此方面，它将更容易地对EEG进行分类，并开发新方法，以增加从限量性数量的限制数量的控制命令的控制命令数量。此处开发的方法将通过对智能轮椅和机器人臂的实时脑部驱动控制进行广泛的实验进行评估。

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

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

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