Data-Driven Multichannel Signal Processing for Brain Computer Interfacing

用于脑机接口的数据驱动多通道信号处理

• 批准号: 21360179
• 负责人: TANAKA Toshihisa
• 金额: $ 11.73万
• 依托单位: Tokyo University of Agriculture and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2009
• 资助国家: 日本
• 起止时间: 2009 至 2011
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21360179/
• 关键词: 信号処理; 脳波計測; インタフェース; ブレイン・コンピュータ・インタフェース; 経験的モード分解; EEG; 脳工学; 脳科学; 時間周波数解析

Brain-computer interfacing is a technology that transmit information in the brain to the outside, has a variety of engineering and medial applications. To realize this technology, we have to establish fundamentals of signal processing for extracting necessary components from the observed electroencephalogram(EEG) and/or for removing unnecessary components. In this KAKENHI project, we paid attention into the empirical mode decomposition(EMD), which is a recently well-known data-driven signal decomposition technique, and we aimed at establishing methodologies that apply this promising technique to signal processing for BCI. As a result of this project, we have succeeded in an accurate separation of electrocardiogram(EOG) components from the observed EEG. Moreover, we have successfully developed a method for extracting necessary components from EEG signals during motor-imagery tasks. Additionally, we have developed and implemented on-line BCI platforms that utilize an Android smartphone and simple robots.

脑机接口是将脑内信息传输到外界的一种技术，具有广泛的工程和医学应用。为了实现这一技术，我们必须建立信号处理的基本原理，用于从观察到的脑电图（EEG）中提取必要的成分和/或去除不必要的成分。在这个KAKENHI项目中，我们关注到经验模式分解（EMD），这是一个最近众所周知的数据驱动的信号分解技术，我们的目标是建立方法，将这种有前途的技术应用到BCI的信号处理。由于这个项目，我们已经成功地在一个准确的分离心电图（EOG）的组成部分，从观察到的EEG。此外，我们已经成功地开发了一种方法，从脑电信号中提取必要的组件在运动想象任务。此外，我们还开发和实施了在线BCI平台，该平台利用Android智能手机和简单的机器人。

项目成果

Empirical Mode Decomposition Method for MEG Phantom Data Analysis

• DOI: 10.1142/s0218126609005794
• 发表时间: 2009-12
• 期刊: J. Circuits Syst. Comput.
• 作者: Ju-Hong Yang;Yuki Saito;Qi-Wei Shi;Jianting Cao;Toshihisa Tanaka;T. Takeda

Seperation of EOG artifacts from EEG signals using bivariate EMD

使用双变量 EMD 将 EOG 伪影与 EEG 信号分离

• 发表时间: 2010
• 期刊: Proc. of 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP 2010)
• 作者: Md. K. I. Molla;T. Tanaka;T. M. Rutkowski;and A. Cichocki
• 通讯作者: and A. Cichocki

Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface

• DOI: 10.1016/j.neulet.2009.11.039
• 发表时间: 2010-01-18
• 期刊: NEUROSCIENCE LETTERS
• 影响因子: 2.5
• 作者: Bakardjian, Hovagim;Tanaka, Toshihisa;Cichocki, Andrzej
• 通讯作者: Cichocki, Andrzej

EEG data analysis based on EMD for coma and quasi-brain-death patients

• DOI: 10.1080/0952813x.2010.506289
• 发表时间: 2011-01-01
• 期刊: JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
• 影响因子: 2.2
• 作者: Shi, Qiwei;Yang, Juhong;Zhu, Huili
• 通讯作者: Zhu, Huili

Interactive components extraction from fEEG, fNIRS, and peripheral biosignals for affective brain-machine interfacing paradigms

从 fEEG、fNIRS 和外围生物信号中提取交互成分，用于情感脑机接口范例

• 发表时间: 2011
• 期刊: Computers in Human Behavior
• 影响因子: 9.9
• 作者: T. M. Rutkowski;T. Tanaka;A. Cichocki;D. Erickson;and D. P. Mandic
• 通讯作者: and D. P. Mandic