Multilinear Subspace Regression and Its Application in BCI.

多线性子空间回归及其在 BCI 中的应用。

• 批准号: 24700154
• 负责人: ZHAO QIBIN
• 金额: $ 2.83万
• 依托单位: Institute of Physical and Chemical Research
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Young Scientists (B)
• 财政年份: 2012
• 资助国家: 日本
• 起止时间: 2012-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-24700154/
• 关键词: Machine learning; Tensor analysis; Kernel machines; Brain computer interface; Brain signal processing; Bayesian inference; EEG Hyperscanning

This project focuses on two aspects: 1) Multiway data (tensor) analysis methods. We proposed and developed many supervised learning methods for tensor data, which can perform multilinear regression or classification on multi-dimensional structured data. In addition, in order to capture the nonlinear relations of tensor data, we proposed a family of kernel functions that can handle tensor-valued inputs, which opens a door for applying kernel machines to tensor space. 2) Brain computer interface and brain signal analysis. We developed an affective brain computer interface (BCI) using emotional faces as stimuli. On the other hand, we extensively applied our proposed tensor techniques for analyzing brain signals, which have shown significant improvement of performance in terms of decoding of brain signals, feature extractions and classifications of ERPs.

本项目主要集中在两个方面：1)多向数据(张量)分析方法。我们提出和发展了许多张量数据的监督学习方法，可以对多维结构化数据进行多线性回归或分类。此外，为了捕捉张量数据的非线性关系，我们提出了一类可以处理张量值输入的核函数，为核机器在张量空间中的应用打开了大门。2)脑-机接口和脑信号分析。我们开发了一种使用情绪面孔作为刺激的情感脑机接口(BCI)。另一方面，我们将我们提出的张量技术广泛应用于脑信号的分析，在脑信号的解码、特征提取和事件相关电位的分类方面都显示出显著的性能改进。

项目成果

Book Chapter in Brain-Computer Interface Research, An affective BCI using multiple ERP components associated to facial emotion processin

脑机接口研究中的书籍章节，使用与面部情绪处理相关的多个 ERP 组件的情感 BCI

• 发表时间: 2013
• 作者: Q. Zhao;A. Onishi;Y. Zhang;and A. Cichocki
• 通讯作者: and A. Cichocki

Auditory Spatial Localization BCI paradigm Classification Enhancement based on Optimization of Brain Evoked Potential Latencies

基于脑诱发电位潜伏期优化的听觉空间定位BCI范式分类增强

• 发表时间: 2013
• 期刊: Cognitive Computation
• 影响因子: 5.4
• 作者: Zhenyu Cai;Shoji Makino;Tomasz M. Rutkowski
• 通讯作者: Tomasz M. Rutkowski

Kernel-based partial least squares for reconstruction of limb movements

基于核的偏最小二乘重建肢体运动

• 发表时间: 2013
• 作者: Q. Zhao;G. Zhou;T. Adali;L. Zhang;and A. Cichocki
• 通讯作者: and A. Cichocki

Patients’ consciousness analysis using dynamic approximate entropy and MEMD method

使用动态近似熵和MEMD方法进行患者意识分析

• 发表时间: 2013
• 作者: G. Cui;Y. Yin;Q. Zhao;A. Cichocki;and J. Cao
• 通讯作者: and J. Cao

A Tensor-Variate Gaussian Process for Classification of Multidimensional Structured Data

• DOI: 10.1609/aaai.v27i1.8568
• 发表时间: 2013-06
• 作者: Qibin Zhao;Liqing Zhang;A. Cichocki