Common and Individual Feature Analysis and Its Applications in BCI

2015 Fiscal Year Final Research Report

• Project/Area Number: 26730125
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Intelligent informatics
• Research Institution: Institute of Physical and Chemical Research
• Principal Investigator: ZHOU GUOXU 国立研究開発法人理化学研究所, 脳科学総合研究センター, 研究員 (80608034)
• Project Period (FY): 2014-04-01 – 2016-03-31
• Keywords: データ統合 / 共通・個別特徴解析 / 結合成分解析 / テンソル分解

Outline of Final Research Achievements

(1) We proposed a new framework for the common and individual features analysis (CIFA) of multi-block linked data. (2) We developed several efficient algorithms for tensor decompositions, which can be used to extract common features from multi-block high-dimensional data where these common features are dominant. (3) We built a steady-state visual evoked potential (SSVEP) based BCI system and recorded EEG signals of multiple subjects and trials which were under same stimuli. These data were analyzed by CIFA, and we achieved so-far the highest accuracy in target frequency recognition.

Free Research Field

情報学