Extended ICA removes artifacts from electroencephalographic recordings,” (1998)

by T P Jung, C Humphries, T W Lee, S Makeig, M J McKeown, V Iragui, T J Sejnowski
Venue:in Advances in Neural Information Processing Systems,
Add To MetaCart
Results 1 - 10 of 62

Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources

by Te-won Lee, Mark Girolami, Terrence J. Sejnowski , 1999
"... An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a proj ..."
Abstract - Cited by 314 (22 self) - Add to MetaCart
An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.

Independent component approach to the analysis of EEG and MEG recordings

by Ricardo Vigário, Jaakko Särelä, Veikko Jousmäki, Matti Hämäläinen, Erkki Oja - IEEE Transactions on Biomedical Engineering , 2000
"... This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish t ..."
Abstract - Cited by 101 (9 self) - Add to MetaCart
This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to
(Show Context)

Independent component analysis for identification of artifacts in magnetoencephalographic recordings

by Ricardo Vigario , ; Veikko , J Ousmiiki2 , Matti Hiimiiliiinen2 , Riitta Hari , Erkki Oja - Proc. NIPS’97 , 1998
"... Abstract We have studied the application of an independent component analysis (ICA) approach to the identification and possible removal of artifacts from a magnetoencephalographic (MEG) recording. This statistical technique separates components according to the kurtosis of their amplitude distribut ..."
Abstract - Cited by 65 (6 self) - Add to MetaCart
Abstract We have studied the application of an independent component analysis (ICA) approach to the identification and possible removal of artifacts from a magnetoencephalographic (MEG) recording. This statistical technique separates components according to the kurtosis of their amplitude distributions over time, thus distinguishing between strictly periodical signals, and regularly and irregularly occurring signals. Many artifacts belong to the last category. In order to assess the effectiveness of the method, controlled artifacts were produced, which included saccadic eye movements and blinks, increased muscular tension due to biting and the presence of a digital watch inside the magnetically shielded room. The results demonstrate the capability of the method to identify and clearly isolate the produced artifacts.

Independent Components of Magnetoencephalography: Localization

by Akaysha C. Tang, Barak A. Pearlmutter, Natalie A. Malaszenko, Dan B. Phung, Bethany C. Reeb , 2002
"... We applied second-order blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification ..."
Abstract - Cited by 44 (12 self) - Add to MetaCart
We applied second-order blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole (ECD) modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations.

EEG-based drowsiness estimation for safety driving using independent component analysis

by Chin-teng Lin, Ruei-cheng Wu, Sheng-fu Liang, Wen-hung Chao, Yu-jie Chen, Tzyy-ping Jung - IEEE Trans. Circuits Syst. I, Reg. Papers , 2005
"... Abstract—Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers’ cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major ..."
Abstract - Cited by 27 (10 self) - Add to MetaCart
Abstract—Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers’ cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver’s cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver’s drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based. Index Terms—Correlation coefficient, drowsiness, electroencephalogram, independent component analysis (ICA), linear
(Show Context)

Dual Multivariate Auto-Regressive Modeling in State Space for Temporal Signal Separation

by Yiu-ming Cheung, Lei Xu , 2003
"... Many existing independent component analysis (ICA) approaches result in deteriorated performance in temporal source separation because they have not taken into consideration of the underlying temporal structure of sources. In this paper, we model temporal sources as a general multivariate auto-regre ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
Many existing independent component analysis (ICA) approaches result in deteriorated performance in temporal source separation because they have not taken into consideration of the underlying temporal structure of sources. In this paper, we model temporal sources as a general multivariate auto-regressive (AR) process whereby an underlying multivariate AR process in observation space is obtained. In this dual AR modeling, the mixing process from temporal sources to observations is the same as the mixture from the nontemporal residuals of the source AR (SAR) process to that of the observation AR (OAR) process. We can therefore avoid the source temporal effects in performing ICA by learning the demixing system on the independently distributed OAR residuals rather than the time-correlated observations. Particularly, we implement this approach by modeling each source signal as a finite mixture of generalized autoregressive conditional heteroskedastic (GARCH) process. The adaptive algorithms are proposed to extract the OAR residuals appropriately online, together with learning the demixing system via a nontemporal ICA algorithm. The experiments have shown its superior performance on temporal source separation.
(Show Context)

Blind Source Separation and Independent Component Analysis: A Review

by Seungjin Choi, Andrzej Cichocki, Hyung-min Park, Soo-young Lee , 2004
"... Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matr ..."
Abstract - Cited by 13 (0 self) - Add to MetaCart
Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree structured graphical models and exploit a priori knowledge about true nature and structure of latent (hidden) variables or sources such as spatio-temporal decorrelation, statistical independence, sparseness, smoothness or lowest complexity in the sense e.g., of best predictability. The possible goal of such decomposition can be considered as the estimation of sources not necessary statistically independent and parameters of a mixing system or more generally as finding a new reduced or hierarchical and structured representation for the observed (sensor) data that can be interpreted as physically meaningful coding or blind source estimation. The key issue is to find a such transformation or coding (linear or nonlinear) which has true physical meaning and interpretation. We present a review of BSS and ICA, including various algorithms for static and dynamic models and their applications. The paper mainly consists of three parts:

Single Trial P300 Recognition in a Virtual Environment

by Jessica D. Bayliss, Dana H. Ballard - University of Rochester , 1998
"... Virtual reality (VR) provides immersive and controllable experimental environments. It expands the bounds of possible evoked potential (EP) experiments by providing complex, dynamic environments in order to study cognition without sacrificing environmental control. The addition of quick, on-line ana ..."
Abstract - Cited by 13 (3 self) - Add to MetaCart
Virtual reality (VR) provides immersive and controllable experimental environments. It expands the bounds of possible evoked potential (EP) experiments by providing complex, dynamic environments in order to study cognition without sacrificing environmental control. The addition of quick, on-line analysis enables feed-back to subjects, making the system we present ideal for safe Brain Computer Interface (BCI) research. In this context, we describe an experiment to recognize the existence of P300 EP epochs at red stoplights and the absence of this signal at yellow stoplights in a virtual driving environment. In order to determine the plausibility of single trial on-line P300 epoch analysis in the artifact ridden driving environment, we have compared the use of Independent Component Analysis (ICA), a Kalman filter, a robust Kalman filter, and correlation with the stoplight averages for recognition ability off-line. We report that while all methods perform better than correlation, the robu...

MICA: Multimodal Independent Component Analysis

by Shotaro Akaho, Yasuhiko Kiuchi, Shinji Umeyama , 1999
"... We extend the framework of ICA (independent component analysis) to the case that there is a pair of information sources. The goal of MICA is to extract statistically dependent pairs of features from the sources, where the components of feature vector extracted from each source are independent. There ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
We extend the framework of ICA (independent component analysis) to the case that there is a pair of information sources. The goal of MICA is to extract statistically dependent pairs of features from the sources, where the components of feature vector extracted from each source are independent. Therefore, the cost function is constructed to maximize the degree of pairwise dependence as well as optimizing the cost function of ICA. We approximate the cost function by two dimensional Gram-Charlier expansion and propose a gradient descent algorithm derived by Amari's natural gradient. The relation between MICA and traditional CCA (canonical correlation analysis) is similar to the relation between ICA and PCA (principal component analysis) . Introduction Recently, ICA (independent component analysis) has attracted a lot of attention because of its applications to brain science. One important application is data analysis for biomedical recordings (e.g. EEG, MEG, fMRI), in which artifactual ...

Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks

by Chin-teng Lin, Li-wei Ko, I-fang Chung, Teng-yi Huang, Yu-chieh Chen, Tzyy-ping Jung, Sheng-fu Liang
"... Abstract—Drivers ’ fatigue has been implicated as a causal factor in many accidents. The development of human cognitive state monitoring system for the drivers to prevent accidents behind the steering wheel has become a major focus in the field of safety driving. It requires a technique that can con ..."
Abstract - Cited by 10 (2 self) - Add to MetaCart
Abstract—Drivers ’ fatigue has been implicated as a causal factor in many accidents. The development of human cognitive state monitoring system for the drivers to prevent accidents behind the steering wheel has become a major focus in the field of safety driving. It requires a technique that can continuously monitor and estimate the alertness level of drivers. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of the complicated noise in a realistic and dynamic driving environment. An adaptive alertness estimation methodology based on electroencephalogram, power spectrum analysis, independent component analysis (ICA), and fuzzy neural network (FNNs) models is proposed in this paper for continuously monitoring driver’s drowsiness level with concurrent changes in the alertness level. A novel adaptive feature selection mechanism is developed for automatically selecting effective frequency
(Show Context)