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- 저자 : Andrzej Cichocki (검색결과 6 건)
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GOM-Face: GKP, EOG, and EMG-Based Multimodal Interface With Application to Humanoid Robot Control
Nam, Yunjun,Koo, Bonkon,Cichocki, Andrzej,Choi, Seungjin IEEE 2014 IEEE Transactions on Biomedical Engineering Vol.61 No.2
<P>We present a novel human-machine interface, called GOM-Face , and its application to humanoid robot control. The GOM-Face bases its interfacing on three electric potentials measured on the face: 1) glossokinetic potential (GKP), which involves the tongue movement; 2) electrooculogram (EOG), which involves the eye movement; 3) electromyogram, which involves the teeth clenching. Each potential has been individually used for assistive interfacing to provide persons with limb motor disabilities or even complete quadriplegia an alternative communication channel. However, to the best of our knowledge, GOM-Face is the first interface that exploits all these potentials together. We resolved the interference between GKP and EOG by extracting discriminative features from two covariance matrices: a tongue-movement-only data matrix and eye-movement-only data matrix. With the feature extraction method, GOM-Face can detect four kinds of horizontal tongue or eye movements with an accuracy of 86.7% within 2.77 s. We demonstrated the applicability of the GOM-Face to humanoid robot control: users were able to communicate with the robot by selecting from a predefined menu using the eye and tongue movements.</P>
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Tongue-Rudder: A Glossokinetic-Potential-Based Tongue–Machine Interface
Nam, Yunjun,Zhao, Qibin,Cichocki, Andrzej,Choi, Seungjin IEEE 2012 IEEE Transactions on Biomedical Engineering Vol.59 No.1
<P>Glossokinetic potentials (GKPs) are electric potential responses generated by tongue movement. In this study, we use these GKPs to automatically detect and estimate tongue positions, and develop a tongue-machine interface. We show that a specific configuration of electrode placement yields discriminative GKPs that vary depending on the direction of the tongue. We develop a linear model to determine the direction of tongue from GKPs, where we seek linear features that are robust to a baseline drift problem by maximizing the ratio of intertask covariance to intersession covariance. We apply our method to the task of wheelchair control, developing a tongue-machine interface for wheelchair control, referred to as tongue-rudder. A teeth clenching detection system, using electromyography, was also implemented in the system in order to assign teeth clenching as the stop command. Experiments on off-line cursor control and online wheelchair control confirm the unique advantages of our method, such as: 1) noninvasiveness, 2) fine controllability, and 3) ability to integrate with other EEG-based interface systems.</P>
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Comparison of Independent Component Analysis and Blind Source Separation Algorithms
Oh, Sang-Hoon,Cichocki, Andrzej,Choi, Seungjin,Lee, Soo-Young 목원대학교 멀티미디어신기술연구소 2002 멀티미디어신기술연구소논문집 Vol.2 No.1
Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for recently developed BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, and the numbers of sources and training samples. We propose several benchmarks which are useful for the evaluation of the algorithms. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and VLSI implementation of these algorithms.
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Nonnegative tensor factorization for continuous EEG classification.
Lee, Hyekyoung,Kim, Yong-Deok,Cichocki, Andrzej,Choi, Seungjin World Scientific 2007 International journal of neural systems Vol.17 No.4
<P>In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.</P>
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잡음환경에서 독립성분 분석과 암묵신호분리 알고리즘의 성능비교
오상훈,최승진,이수영,O, Sang-Hun,Cichocki, Andrzej,Choe, Seung-Jin,Lee, Su-Yeong 대한전자공학회 2002 電子工學會論文誌-CI (Computer and Information) Vol.39 No.2
여러 가지의 독립성분분석 및 암묵신호분리 알고리즘들이 개발되었지만, 아직 이러한 알고리즘들의 성능비교가 철저히 이루어지지는 못 하였다. 이 논문은 이 알고리즘들 중에서 뛰어난 알고리즘들을 센서 잡음에 대한 강인성, 계산 복잡도, 혼합 행렬의 조건, 센서 수, 학습패턴 수 등 여러 측면에서 비교한다. 또한, 알고리즘들의 성능 비교에 유용한 문제들도 제시한다. 이 비교결과는 이 알고리즘들의 EEG/MEG 분석, 음성신호분리 등과 같은 실질적 응용에 큰 도움이 될 것이다. Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, the number of sensors, and the number of training patterns. We propose several benchmarks which are useful for the evaluation of the algorithm. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and separation of miked speech signals.
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이혜경(Hyekyoung Lee),김용덕(Yong-Deok Kim),Andrzej Cichocki,최승진(Seungjin Choi) 한국정보과학회 2008 정보과학회 컴퓨팅의 실제 논문지 Vol.14 No.5
본 논문에서는 연속적인 뇌파 분류를 위해 비음수 텐서 분해를 이용한 특징 추출과 비터비 알고리즘을 이용한 연속적인 데이타의 클래스 분류를 결합한 새로운 알고리즘을 제시한다. 비음수 텐서 분해는 이미 스펙트럼 데이타에 대해 뇌파의 주요한 특징을 잘 추출한다고 알려진 비음수 행렬 분해의 확장으로써 행렬이라는 제한된 틀에서 벗어나 데이타가 가지는 다양한 차원으로의 확대가 가능하다. 뇌-컴퓨터 인터페이스 컴피티션을 통해 공개된 데이타를 이용한 실험을 통해 제안된 방법의 유용함을 증명하도록 하겠다. In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.
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