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自校准集成方法，该方法在硬件，信号处理和用户界面层面上运行，以适应新的记录会话，同时对用户的负担最小，如下所述：1）硬件和电路级方法，其目的是通过注入已知幅度的参考信号并观察共模抑制比（CMMR）来发现输入触点失配电路和电极。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脑电（EEG）信号从一个会话到另一个会话的强烈变化。具体来说，研究小组提出了自适应训练算法，利用先前记录会话的相关信息来缩短甚至省略下一个会话的校准时间。3)创建可定制的用户界面，以产生更友好的用户界面，减少用户的负担。更自适应的用户界面将导致更舒适的使用，更高的传输速率和更好的准确性，在实现用户的意图。研究人员计划开发一种廉价、易于佩戴、低功耗的脑机接口（BCI）系统，该系统使用干接触EEG电极，可通过蓝牙连接到计算机，适合实时应用。EEG系统已经存在了相对较长的时间，其应用主要集中在实验室内。然而，BCI应用程序可以潜在地包括我们日常生活中的任何真实世界的交互。低姿态，廉价的BCI设备的引入，手机大小和可比的价格为我们的思想，表情和情感控制的新应用创造了机会。例如，随着慢性病发病率的上升，BCI自校准设备的主要医疗保健应用是可穿戴的家庭评估系统，以通过长期EEG记录和分析来量化症状的存在或大脑缺陷的治疗效果。脑机接口技术有很大的潜力成为用户与计算机交互的最常见的通信替代方案。例如，BCI设备能够在游戏行业中出现。它使消费者能够体验一种全新的人机交互形式，消除了传统的游戏，娱乐，导航和康复。