Independent components of magnetoencephalography: single-trial response onset times

Werecently demonstrated that second-order blind identification (SOBI), an independent component analysis (ICA) method, can separate the mixture of neuronal and noise signals in magnetoencephalographic (MEG) data into neuroanatomically and neurophysiologically meaningful components. When the neuronal signals had relatively higher trial-to-trial variability, SOBI offered a particular advantage in identifying and localizing neuronal source activations with increased source detectability (A. C. Tang et al., 2002, Neural Comput. 14, 1827-1858). Here, we explore the utility of SOBI in the analysis of temporal aspects of neuromagnetic signals from MEG data. From SOBI components, we were able to measure single-trial response onset times of neuronal populations in visual, auditory, and somatosensory modalities during cognitive and sensory activation tasks, with a detection rate as high as 96% under optimal conditions. Comparing the SOBI-aided detection results with those obtained directly from the sensors, we found that with SOBI preprocessing, we were able to measure, among a greater proportion of trials, single-trial response onset times that are above background neuronal activity. We suggest that SOBI ICA can improve our current capability in measuring single-trial responses from human subjects using the noninvasive brain imaging method MEG.     

Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more “interesting” sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method.     

基于独立元分析的脑磁图数据分析和处理(英文)

背景:诱发响应信号是由刺激的时间锁定的,对于一些特定的刺激呈现小的个人差距,脑磁图数据中诱发响应的提取对人脑功能的认识很重要。目的:将独立元分析应用于分离混迭的脑磁图多通道信号中的信号源,提出一个简单有效的基于独立元分析的脑磁图数据分析和处理方法。设计:单一样本分析。单位:复旦大学电子工程系和复旦大学脑科学研究中心。对象:实验于2002-09在日本通信综合研究所关西先端研究中心完成,选择日本东京药科大学的健康志愿者1例,男性;年龄23岁。受试者自愿参加。方法:①对脑磁图进行必要的预处理,如低通滤波和主成分分解。②采用独立元分析的方法对取自148个通道的脑磁图的数据进行分析和处理,尤其是诱发反应的提取。③对提取的各独立成分进行周期平均。主要观察指标:应用独立元分析方法对脑磁图数据分析。结果:①脑磁图信号有较高的冗余度,信号能量的绝大部分集中在前30个主成分中,从前30个主成分中抽取干扰源和诱发响应活动源。②眼动干扰源仍被清楚地检测和分离在第1个独立元中,心电干扰被分离在第20个独立元中。③α波呈现在第2,3,7和9等独立元中。波(13～30Hz)呈现在第11和第12独立元中。④诱发响应是响应于刺激的周期性波形,集中在第5独立元中。结论:利用独立元分析,可从混迭的脑磁图数据中分离这些干扰源,更进一步,消除这些干扰成分,可得到净化的脑磁图数据。借助独立元分析,有效的分离α波、β波以及眼动、眨眼等神经活动源,有可能为它们的脑神经活动研究提供新的方法和途径。利用独立元分析方法成功的进行了听觉诱发反应的分离和提取。     

Let's face it, from trial to trial: Comparing procedures for N170 single-trial estimation

The estimation of event-related single trial EEG activity is notoriously difficult but is of growing interest in various areas of cognitive neuroscience, such as multimodal neuroimaging and EEG-based brain computer interfaces. However, an objective evaluation of different approaches is lacking. The present study therefore compared four frequently-used single-trial data filtering procedures: raw sensor amplitudes, regression-based estimation, bandpass filtering, and independent component analysis (ICA). High-density EEG data were recorded from 20 healthy participants in a face recognition task and were analyzed with a focus on the face-selective N170 single-trial event-related potential. Linear discriminant analysis revealed significantly better single-trial estimation for ICA compared to raw sensor amplitudes, whereas the other two approaches did not improve classification accuracy. Further analyses suggested that ICA enabled extraction of a face-sensitive independent component in each participant, which led to the superior performance in single trial estimation. Additionally, we show that the face-sensitive component does not directly represent activity from a neuronal population exclusively involved in face-processing, but rather the activity of a network involved in general visual processing. We conclude that ICA effectively facilitates the separation of physiological trial-by-trial fluctuations from measurement noise, in particular when the process of interest is reliably reflected in components representing the neural signature of interest.     

Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity

Following the development of increasingly precise measurement instruments and fine-grain analysis tools for electroencephalographic (EEG) data, analysis of single-trial event-related EEG has considerably widened the utility of this non-invasive method to investigate brain activity. Recently, independent component analysis (ICA) has become one of the most prominent techniques for increasing the feasibility of single-trial EEG. This blind source separation technique extracts statistically independent components (ICs) from the EEG raw signal. By restricting the signal analysis to those ICs representing the processes of interest, single-trial analysis becomes more flexible. Still, the selection-criteria for in- or exclusion of certain ICs are largely subjective and unstandardized, as is the actual selection process itself. We present a rationale for a bottom-up, data-driven IC selection approach, using clear-cut inferential statistics on both temporal and spatial information to identify components that significantly contribute to a certain event-related brain potential (ERP). With time-range being the only necessary input, this approach considerably reduces the pre-assumptions for IC selection and promotes greater objectivity of the selection process itself. To test the validity of the approach presented here, we present results from a simulation and re-analyze data from a previously published ERP experiment on error processing. We compare the ERP-based IC selections made by our approach to the selection made based on mere signal power. The comparison of ERP integrity, signal-to-noise ratio, and single-trial properties of the back-projected ICs outlines the validity of the approach presented here. In addition, functional validity of the extracted error-related EEG signal is tested by investigating whether it is predictive for subsequent behavioural adjustments.     

Identifying reliable independent components via split-half comparisons

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method's validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline.     

ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization

The extraction of event-related oscillatory neuromagnetic activities from single-trial measurement is challenging due to the non-phase-locked nature and variability from trial to trial. The present study presents a method based on independent component analysis (ICA) and the use of a template-based correlation approach to extract Rolandic beta rhythm from magnetoencephalographic (MEG) measurements of right finger lifting. A single trial recording was decomposed into a set of coupled temporal independent components and corresponding spatial maps using ICA and the reactive beta frequency band for each trial identified using a two-spectrum comparison between the postmovement interval and a reference period. Task-related components survived dual criteria of high correlation with both the temporal and the spatial templates with an acceptance rate of about 80%. Phase and amplitude information for noise-free MEG beta activities were preserved not only for optimal calculation of beta rebound (event-related synchronization) but also for profound penetration into subtle dynamics across trials. Given the high signal-to-noise ratio (SNR) of this method, various methods of source estimation were used on reconstructed single-trial data and the source loci coherently anchored in the vicinity of the primary motor area. This method promises the possibility of a window into the intricate brain dynamics of motor control mechanisms and the cortical pathophysiology of movement disorder on a trial-by-trial basis.     

Testing the ICA mixing matrix based on inter-subject or inter-session consistency

Independent component analysis (ICA) is increasingly used for analyzing brain imaging data. ICA typically gives a large number of components, many of which may be just random, due to insufficient sample size, violations of the model, or algorithmic problems. Few methods are available for computing the statistical significance (reliability) of the components. We propose to approach this problem by performing ICA separately on a number of subjects, and finding components which are sufficiently consistent (similar) over subjects. Similarity is defined here as the similarity of the mixing coefficients, which usually correspond to spatial patterns in EEG and MEG. The threshold of what is "sufficient" is rigorously defined by a null hypothesis under which the independent components are random orthogonal components in the whitened space. Components which are consistent in different subjects are found by clustering under the constraint that a cluster can only contain one source from each subject, and by constraining the number of the false positives based on the null hypothesis. Instead of different subjects, the method can also be applied on different recording sessions from a single subject. The testing method is particularly applicable to EEG and MEG analysis.     

基于ICA的脑电信号去噪方法研究与应用

目的在脑电信号的采集和处理过程中,受到各种各样噪声的影响,为有效地提取和分析检测信号中的有用信息,提出采用独立分量分析(independentcomponentanalysis,ICA)的方法对脑电信号进行去噪处理。方法工频噪声、心电伪迹以及脑电波源信号之间的关系是统计独立的,满足ICA方法的分离条件,可将脑电信号去噪问题转化为独立分量分离问题,通过构造与工频噪声频率相同的正交正弦和余弦信号作为对工频噪声的参考信号,将构造的两个参考信号和心电信号以及含噪脑电信号作为ICA中混合矩阵的输入信号,采用收敛速度快的FastICA算法把脑电信号中的工频噪声和心电伪迹作为独立信号分离出去,得到去噪后的脑电信号。结果通过ICA方法对噪声进行分离后,脑电信号中的两种噪声基本被消除,并且可很好地保留脑电信号有用成分。结论将ICA的方法用于去除脑电信号中的多种噪声成分是有效的、可行的。     

Mapping single-trial EEG records on the cortical surface through a spatiotemporal modality

Event-related potentials (ERPs) induced by visual perception and cognitive tasks have been extensively studied in neuropsychological experiments. ERP activities time-locked to stimulus presentation and task performance are often observed separately at individual scalp channels based on averaged time series across epochs and experimental subjects. An analysis using averaged EEG dynamics could discount information regarding interdependency between ongoing EEG and salient ERP features. Advanced tools such as independent component analysis (ICA) have been developed for decomposing collections of single-trial EEG records into separate features. Those features (or independent components) can then be mapped onto the cortical surface using source localization algorithms to visualize brain activation maps and to study between-subject consistency. In this study, we propose a statistical framework for estimating the time course of spatiotemporally independent EEG components simultaneously with their cortical distributions. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the Electromagnetic Spatiotemporal ICA (EMSICA) method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus.     

Understanding brain connectivity from EEG data by identifying systems composed of interacting sources

In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the head. The effects of volume conduction produce spurious interactions in the measured signals. It is fundamental to separate true source interactions from noise and to unmix the contribution of different systems composed by interacting sources in order to understand interference mechanisms. As a prerequisite, we consider the problem of unmixing the contribution of uncorrelated sources to a measured field. This problem is equivalent to the problem of unmixing the contribution of different uncorrelated compound systems composed by interacting sources. To this end, we develop a principal component analysis-based method, namely, the source principal component analysis (sPCA), which exploits the underlying assumption of orthogonality for sources, estimated from linear inverse methods, for the extraction of essential features in signal space. We then consider the problem of demixing the contribution of correlated sources that comprise each of the compound systems identified by using sPCA. While the sPCA orthogonality assumption is sufficient to separate uncorrelated systems, it cannot separate the individual components within each system. To address that problem, we introduce the Minimum Overlap Component Analysis (MOCA), employing a pure spatial criterion to unmix pairs of correlates (or coherent) sources. The proposed methods are tested in simulations and applied to EEG data from human micro and alpha rhythms.     

A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition

Neuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain. However, studying these oscillations is notoriously difficult with EEG/MEG recordings due to a massive overlap of activity from multiple sources and also due to the strong background noise. Here we present a novel method for the reliable and fast extraction of neuronal oscillations from multi-channel EEG/MEG/LFP recordings. The method is based on a linear decomposition of recordings: it maximizes the signal power at a peak frequency while simultaneously minimizing it at the neighboring, surrounding frequency bins. Such procedure leads to the optimization of signal-to-noise ratio and allows extraction of components with a characteristic "peaky" spectral profile, which is typical for oscillatory processes. We refer to this method as spatio-spectral decomposition (SSD). Our simulations demonstrate that the method allows extraction of oscillatory signals even with a signal-to-noise ratio as low as 1:10. The SSD also outperformed conventional approaches based on independent component analysis. Using real EEG data we also show that SSD allows extraction of neuronal oscillations (e.g., in alpha frequency range) with high signal-to-noise ratio and with the spatial patterns corresponding to central and occipito-parietal sources. Importantly, running time for SSD is only a few milliseconds, which clearly distinguishes it from other extraction techniques usually requiring minutes or even hours of computational time. Due to the high accuracy and speed, we suggest that SSD can be used as a reliable method for the extraction of neuronal oscillations from multi-channel electrophysiological recordings.     

Real-time neural activity and connectivity in healthy individuals and schizophrenia patients

Processing of facial information is distributed across several brain regions, as has been shown recently in many neuroimaging studies. Disturbances in accurate face processing have been repeatedly demonstrated in different stages of schizophrenia. Recently, electroencephalography (EEG) and tomographic analysis of average magnetoencephalographic (MEG) data were used to define the latencies of significant regional brain activations in healthy and schizophrenic subjects elicited during the recognition of facial expression of emotions. The current study re-examines these results using tomographic analysis of single trial MEG data. In addition to the areas identified by the analysis of the average MEG data, statistically significant activity is identified in several other areas, including a sustained increase in the right amygdala activity in response to emotional faces in schizophrenic subjects. The single trial analysis demonstrated that the reduced activations identified from the average MEG signal of schizophrenic subjects is due to high variability across single trials rather than reduced activity in each single trial. In control subjects, direct measures of linkage demonstrate distinct stages of processing of emotional faces within well-defined network of brain regions. Activity in each node of the network, confined to 30 to 40 ms latency windows, is linked to earlier and later activations of the other nodes of the network. In schizophrenic subjects, no such well-defined stages of processing were observed. Instead, the activations, although strong were poorly linked to each other, managing only isolated links between pairs of areas.     

ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner

Electroencephalogram (EEG) data acquired in the MRI scanner contains significant artifacts, one of the most prominent of which is ballistocardiogram (BCG) artifact. BCG artifacts are generated by movement of EEG electrodes inside the magnetic field due to pulsatile changes in blood flow tied to the cardiac cycle. Independent Component Analysis (ICA) is a statistical algorithm that is useful for removing artifacts that are linearly and independently mixed with signals of interest. Here, we demonstrate and validate the usefulness of ICA in removing BCG artifacts from EEG data acquired in the MRI scanner. In accordance with our hypothesis that BCG artifacts are physiologically independent from EEG, it was found that ICA consistently resulted in five to six independent components representing the BCG artifact. Following removal of these components, a significant reduction in spectral power at frequencies associated with the BCG artifact was observed. We also show that our ICA-based procedures perform significantly better than noise-cancellation methods that rely on estimation and subtraction of averaged artifact waveforms from the recorded EEG. Additionally, the proposed ICA-based method has the advantage that it is useful in situations where ECG reference signals are corrupted or not available.     

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.     

Improving MEG source localizations: an automated method for complete artifact removal based on independent component analysis

The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.     

Validating the independent components of neuroimaging time series via clustering and visualization

Recently, independent component analysis (ICA) has been widely used in the analysis of brain imaging data. An important problem with most ICA algorithms is, however, that they are stochastic; that is, their results may be somewhat different in different runs of the algorithm. Thus, the outputs of a single run of an ICA algorithm should be interpreted with some reserve, and further analysis of the algorithmic reliability of the components is needed. Moreover, as with any statistical method, the results are affected by the random sampling of the data, and some analysis of the statistical significance or reliability should be done as well. Here we present a method for assessing both the algorithmic and statistical reliability of estimated independent components. The method is based on running the ICA algorithm many times with slightly different conditions and visualizing the clustering structure of the obtained components in the signal space. In experiments with magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) data, the method was able to show that expected components are reliable; furthermore, it pointed out components whose interpretation was not obvious but whose reliability should incite the experimenter to investigate the underlying technical or physical phenomena. The method is implemented in a software package called Icasso.     

fMRI-EEG integrated cortical source imaging by use of time-variant spatial constraints

In response to the need of establishing a high-resolution spatiotemporal neuroimaging technique, tremendous efforts have been focused on developing multimodal strategies that combine the complementary advantages of high-spatial-resolution functional magnetic resonance imaging (fMRI) and high-temporal-resolution electroencephalography (EEG) or magnetoencephalography (MEG). A critical challenge to the fMRI-EEG/MEG integration lies in the spatial mismatches between fMRI activations and instantaneous electrical source activities. Such mismatches are fundamentally due to the fact that fMRI and EEG/MEG signals are generated and collected in highly different time scales. In this paper, we propose a new theoretical framework to solve the problem of fMRI-EEG integrated cortical source imaging. The new framework has two principal technical advancements. First, by assuming a linear neurovascular coupling, a method is derived to quantify the fMRI signal in each voxel as proportional to the time integral of the power of local electrical current during the period of event-related potentials (ERP). Second, the EEG inverse problem is solved for every time instant using an adaptive Wiener filter, in which the prior time-variant source covariance matrix is estimated by combining the quantified fMRI responses and the segmented EEG signals before response averaging. A series of computer simulations were conducted to evaluate the proposed methods in terms of imaging the instantaneous cortical current density (CCD) distribution and estimating the source time courses with a millisecond temporal resolution. As shown in the simulation results, the instantaneous CCD reconstruction by using the proposed fMRI-EEG integration method was robust against both fMRI false positives and false negatives while retaining a spatial resolution nearly as high as that of fMRI. The proposed method could also reliably estimate the source waveforms when multiple sources were temporally correlated or uncorrelated, or were sustained or transient, or had some features in frequency or phase, or had even more complicated temporal dynamics. Moreover, applying the proposed method to real fMRI and EEG data acquired in a visual experiment yielded a time series of reconstructed CCD images, in agreement with the traditional view of hierarchical visual processing. In conclusion, the proposed method provides a reliable technique for the fMRI-EEG integration and represents a significant advancement over the conventional fMRI-weighted EEG (or MEG) source imaging techniques and is also applicable to the fMRI-MEG integrated source imaging.     

Inferring task-related networks using independent component analysis in magnetoencephalography

A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data.     

Group independent component analysis of language fMRI from word generation tasks

Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be particularly relevant to pre-surgical planning, by falsely indicating low surgical risk in areas where no activations are shown. Furthermore, since the execution of language paradigms involves cognitive processes other than language function per se, the conventional general linear model (GLM) method may identify non-language-specific activations. In this study, we assessed an exploratory approach, independent component analysis (ICA), as a potential complementary method to the inferential GLM method in language mapping applications. We specifically investigated whether this approach might reduce type II error as well as generate more language-specific maps. Fourteen right-handed healthy subjects were studied with fMRI during two word generation tasks. A similarity analysis across tasks was proposed to select components of interest. Union analysis was performed on the language-specific components to increase sensitivity, and conjunction analysis was performed to identify language areas more likely to be essential. Compared with GLM, ICA identified more activated voxels in the putative language areas, and signals from other sources were isolated into different components. Encouraging results from one brain tumor patient are also presented. ICA may be used as a complementary tool to GLM in improving pre-surgical language mapping.