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| 1 | EEG-based asynchronous BCI control of a car in 3D virtual reality environments显示文摘Brain computer interface (BCI) aims at creating new communication channels without depending on brain’s normal output channels of peripheral nerves and muscles. However,natural and sophisticated interactions manner between brain and computer still remain challenging. In this paper,we investigate how the duration of event-related desynchronization/synchronization (ERD/ERS) caused by motor im-agery (MI) can be modulated and used as an additional control parameter beyond simple binary deci-sions. Furthermore,using the non-time-locked properties of sustained (de)synchronization,we have developed an asynchronous BCI system for driving a car in 3D virtual reality environment (VRE) based on cumulative incremental control strategy. The extensive real time experiments confirmed that our new approach is able to drive smoothly a virtual car within challenging VRE only by the MI tasks with-out involving any muscular activities. | ZHAO QiBin ZHANG LiQing CICHOCKI Andrzej | 2009 | Chinese Science Bulletin2009，54，1： | 14 |
| 2 | 三维虚拟现实环境中基于EEG的异步BCI小车导航系统显示文摘脑机交互(BCI)目的是为了建立一种全新的对外信息交流通路,它不依赖于常规大脑输出通道(外周神经系统及肌肉组织).但是,在大脑与计算机之间建立一种自然的、功能复杂的交互方式仍是一种挑战.本文研究了如何使运动想象(MI)引起的事件相关去同步/同步(ERD/ERS)的持续时间可以被思维任务调制,并提供一种额外的连续控制参数,从而超越了传统的二值控制问题.另外,采用持续去同步的非锁时性,开发了基于累积增量控制策略的异步BCI系统,即三维虚拟现实中小车导航系统.实时实验结果表明,本文所提出的方法能够使受试者采用运动想象EEG在复杂的三维虚拟现实环境中平稳地驾驶小车. | 赵启斌 张丽清 CICHOCKI Andrzej | 2008 | 科学通报2008，53，23： | 3 |
| 3 | On the robustness of EEG tensor completion methods显示文摘During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were designed.Firstly, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or not a completion method is useful enough to complete EEG signals. | DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi | 2021 | Science China(Technological Sciences)2021，64，9： | 2 |
| 4 | Blind separation of nonstationary sources in noisy mixtures 显示文摘 | Choi S Cichocki A | 2000 | Electronics Letters2000，36，9： | 1 |
| 5 | Robust learning algorithm for blind separation of signals显示文摘 | Cichocki A Unbehauen R Rummert E | 1994 | Electronics Letters1994，17，30： | 1 |
| 6 | Sequential blind signal extraction in order specified by stochastic properties 显示文摘 | Cichocki A Thawonmas R Amaxi S | 1997 | Electronics Letters1997，33，1： | 1 |
| 7 | Adaptive blind signal processing-neural network approaches显示文摘 | Amari and Cichocki | 1998 | Proceeding of The IEEE1998，10，86： | 1 |
| 8 | Analysis of sparse representation and blind source separation显示文摘 | LI Y Q CICHOCKI A AMARI S | 2004 | Neural Computation2004，16，6： | 1 |
| 9 | A minor component analysis algorithm 显示文摘 | Luo F L Unbehauen R Cichocki A | 1997 | Neural Networks1997，10，2： | 1 |
| 10 | On neural blind separation with noise suppression and redundancy reduction 显示文摘 | Karhunen J Cichocki A Kasprzak W | 1997 | Int J Neural Systems1997，，8： | 1 |
| 11 | Neural Network for Singular Value Decomposition 显示文摘 | CICHOCKI | 1992 | E- lectronics Letters1992，28，8： | 1 |
| 12 | Analysis of sparse repre-sentation and blind source separation显示文摘 | Li Y Cichocki A Amari S | 2004 | Neural Com-putation2004，16，6： | 1 |
| 13 | Sequential blind signal extraction in order specified by stochastics properties 显示文摘 | A Cichocki R Thawonmas S Amari | 1997 | Electronics Letters (S0013-5194)1997，33，1： | 1 |
| 14 | Sequential blind signal extraction in order specified by stochastic properties显示文摘 | Cichocki A Thawonmas R Amari S | 1997 | Electronics Letters1997，33，1： | 1 |
| 15 | Artificial neural networks for real-time estimation of basic waveforms of voltages and currents显示文摘 | Cichocki A Lobos T | 1994 | IEEE Transactions on Power Systems1994，9，2： | 1 |
| 16 | Improved FOCUSS method with con- jugate gradient iterations 显示文摘 | He Z S Cichocki A | 2009 | IEEE Transactions on Sig- nal Processing2009，57，1： | 1 |
| 17 | Blind source separation and independent component analysis:a review 显示文摘 | CHOI S CICHOCKI A PARK H | 2005 | Neural Information Processing-Letters and Re- views2005，16，1： | 1 |
| 18 | Sparse component analysis and blind source separation of underdetermined mixtures 显示文摘 | P G Georgiev F Theis A Cichocki | 2005 | IEEE Transactions on Neural Network2005，16，4： | 1 |
| 19 | A minor component analysis algorithm显示文摘 | Luo F L Unbehauen R Cichocki A | 1997 | Neural Networks1997，10，2： | 1 |
| 20 | Stability Analysis of Adaptive Blind Source Separation显示文摘 | Amari S I Chen T P Cichocki A | 1997 | Neural Netowrks1997，10，8： | 1 |
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