I-Corps: Self Calibration Techniques for Robust Brain Computer Interface

I-Corps：稳健脑机接口的自校准技术

• 批准号: 1338964
• 负责人: Roozbeh Jafari
• 金额: $ 5万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2014-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1338964&HistoricalAwards=false
• 关键词: Corps; Self; Calibration; Techniques; Robust

Researchers propose a self-calibrating integrated approach that operates at the hardware, signal processing and user interface levels to adapt to the new recording session with the least burden on the user as outlined here: 1) The hardware and circuit level approach, where the aim is to find input contact mismatch by injecting a reference signal of known amplitude and observe the common-mode rejection ration (CMMR) of the circuits and electrodes. Artifacts of this reference signal manifest themselves when the electrode coupling is worsening. 2) Employing the signal processing calibration techniques to resolve the strong variation in electroencephalography (EEG) signals from one session to another. Specifically, the research team proposes adaptive training algorithms to utilize relevant information from prior recording sessions to shorten or even omit the calibration time for the next session. 3) Creating customizable user interface in order to produce a more user friendly interface that create less burden on the user. More adaptive user interfaces will lead to more comfortable use, higher transfer rate and better accuracy in realization of the user intents. Researchers plan to develop an inexpensive, easy-to-wear, and low power brain computer interface (BCI) system that uses dry-contact EEG electrodes and can be connected to the computer via Bluetooth and is suitable for real-time applications.EEG systems have been around for a relatively long time and their applications have been mostly inside the laboratories. However, BCI applications can potentially include any real-world interaction in our daily life. The introduction of low profile, and inexpensive BCI devices with the size of a cellphone and comparable prices create opportunities for new applications controlled with our thoughts, expressions and emotions. For instance, with the rising incidence of chronic diseases, a major health care application for BCI self-calibrating devices is wearable in-home assessment systems to quantify the existence of symptoms or effectiveness of treatments for brain deficiencies through long term EEG recording and analysis. BCI technology has great potentials to become the most common communication alternative for users interacting with computers. For instance, BCI devices are capable of emerging in the gaming industry. It enables the consumers to experience an entirely new form of human-machine interaction by eliminating the conventional joysticks for gaming, entertainment, navigation and rehabilitation.

研究人员提出了一种在硬件，信号处理和用户界面级别上运行的自我校准的集成方法，以适应新的记录会话，以下概述的用户负担最小的负担：1）硬件和电路级别的方法，其目的是通过注入已知的放大器和观察公共模型的注射及其commod compodition and compript and Cmmmr and comprate and Cmmmr）来找到输入接触不匹配的信号。当电极耦合恶化时，该参考信号的伪影显现出来。 2）采用信号处理校准技术来解决从一个会话到另一个会话的脑电图（EEG）信号的强大变化。具体而言，研究团队建议自适应培训算法利用相关信息，从先前的记录会话中缩短甚至省略下次会议的校准时间。 3）创建可自定义的用户界面，以产生更友好的界面，从而减少用户的负担。更高的自适应用户界面将导致更舒适的使用，更高的传输速率以及实现用户意图的更好准确性。研究人员计划开发一种廉价，易穿和低功率大脑计算机界面（BCI）系统，该系统使用干连接EEG电极，并且可以通过蓝牙连接到计算机，并且适用于实时应用。EEEG系统已经存在了相对较长的时间，并且它们的应用程序大多在实验室内。但是，BCI应用程序可能可能包括我们日常生活中的任何现实互动。引入低调和廉价的BCI设备，具有手机的大小和可比的价格，为通过我们的思想，表情和情感控制的新应用程序创造了机会。例如，随着慢性疾病的发病率上升，BCI自我校准设备的主要医疗保健应用是可穿戴的室内评估系统，可以通过长期的EEG记录和分析来量化脑部缺乏症状的症状或有效性。 BCI技术具有成为与计算机交互的用户最常见的沟通替代方案的巨大潜力。例如，BCI设备能够在游戏行业中出现。它使消费者能够消除用于游戏，娱乐，导航和康复的常规操纵杆，从而体验一种全新的人机互动形式。