Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data

Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g., trajectories of activation propagating across cortex. This leads to a model of convolutive signal superposition, in contrast with the commonly used instantaneous mixing model. In the frequency-domain, convolutive mixing is equivalent to multiplicative mixing of complex signal sources within distinct spectral bands. We decompose the recorded spectral-domain signals into independent components by a complex infomax ICA algorithm. First results from a visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics in the data, (2) links to subject behavior, (3) sources with a limited spectral extent, and (4) a higher degree of independence compared to sources derived by standard ICA.Comment: 21 pages, 11 figures. Added final journal reference, fixed minor typo

An Efficient Algorithm by Kurtosis Maximization in Reference-Based Framework

This paper deals with the optimization of kurtosis for complex-valued signals in the independent component analysis (ICA) framework, where source signals are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast schemes, a similar contrast function is put forward, based on which a new fast fixed-point (FastICA) algorithm is proposed. The new optimization method is similar in spirit to the former classical kurtosis-based FastICA algorithm but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. The performance of this new algorithm is confirmed through computer simulations

A Constrained ICA-EMD Model for Group Level fMRI Analysis

Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components

Separation of Gravity Anomaly Data considering Statistical Independence among Signals : Application to Severely Contaminated Data Obtained by Prototype Mobile Gravimeter

The ground motion (GM) characteristics are affected by local subsurface structure. Gravity method is one of the useful methods to know the information on subsurface structure. The gravity anomaly data obtained by gravity survey can be correlated with the lateral variation of subsurface rock densities. For gravity survey, spring type gravimeter has been used so far. This gravimeter gives accurate resolution but they are very expensive and diffcult to handle. Recently, Team Morikawa have developed a prototype mobile gravimeter that uses Force-Balanced (FB) accelerometer. This prototype is light weight, compact, easy to handle and inexpensive. It also offers the resolution that is good enough for preparing gravity map for subsurface modelling. However, unlike the conventional spring-type gravimeter, this newly developed FB gravimeter is highly sensitive to high frequency noise. The observed data by this gravimeter are easily contaminated by various kinds of disturbances in a small size carrier like engine vibration, carrier acceleration, wind velocity and carrier tilting accompanied by sensor drifts, electrical noise etc. The amplitudes of such noises can be upto 100,000 times larger than the gravity anomaly. In order to extract the gravity anomaly from such observation, data processing is essential. Conventionally, the data was observed in a large carrier (ship) on a more stable environment and the sensor was not sensitive to high frequency noise, so the noise contamination was not severe. The data processing techniques like low pass filtering and Second order statistics method (such as SOBI) were used. However, in case of severely contaminated data, low pass filtering might not be enough. SOBI is an advanced blind source separation (BSS) technique that separates source and noise blindly by exploiting the statistical property of data. It separates the target source by assuming that source and unwanted data are un-correlated at various time-lags. The gravity anomaly and other noises are generated from independent physical sources. It can be safely assumed that gravity anomaly and other data are independent but, it can not be strictly claimed that they have no correlation. So, further improvement than second order statistics method is desired. As a scheme of considering independence of signals to blind source separation, Independent Component Analysis (ICA) has been used in the field of BSS since 1990's. It separates the sources by maximizing the independence of linearly transformed observed signals. Both mixing matrix and source signals are identified when only the mixed data are available. Further, independence between signals has nothing to do with their amplitudes. The huge difference in amplitudes among gravity anomaly and noise does not affect their independence. So ICA is suitable for our purpose. ICA renders ambiguity in amplitude of separated signal but this problem has little significance in our case since an appropriate scalar multiple can be estimated with the help of information of gravity at few known points. Thus it is proposed to use ICA for separating gravity anomaly data from its mixture with several noises. The survey data is observed at Toyama bay, Japan. The National Institute of Advanced Industrial Science and Technology (AIST), Japan has provided the gravity map for the same place. This map is used to calculate the reference data that facilitates us to verify the performance of the proposed scheme. The prototype gravimeter consisted of group of sensors. Since ICA requires at least two sets of data, the major data obtained by Analog servo (VSE) was combined with data by other sensors as supplementary data. Following Team Morikawa's approach, the performance of various sensors are compared. The application of low pass filtering(LPF) as a pre-processing to ICA is realized to be important. The presence of high frequency noise in the data is found to be unfavourable for the separation of gravity anomaly data. Both SOBI and ICA work only after the application of LPF. The choice of an appropriate cut-off filter was also observed to affect the results. The combination of VSE data and vertical component of Accelerometer Titan (Taurus-Z) as an iput to ICA gives good result. When other horizontal components were used with VSE data the results are not satisfactory. Further, ICA is found to perform better at certain conditions of data acquisition environment. At the portions when ship motion is unidirectional the trend of ICA separated data is harmonious with reference data. When the ship velocity was lesser while proceeding towards the sea, the ICA result is matching very well with reference data. When the ship was highly unstable during ship stopping time ICA result are deviating away from the reference data. At other relatively stable sections the ICA separated data follows the trend of reference data well. The separation of input data by ICA into different output components verifies that the source gravity anomaly and other data are independent. Thus it satisfies our assumption. The harmony of ICA separated data with trend of reference data at major sections verifies the applicability of ICA, under certain data acquisition environments. The accuracy of properly separated data by ICA is good enough for preparing gravity map for the purpose of subsurface modelling. However, there is still a room for further improvement. An effort is made to study time-frequency characteristics of data without observing any clear merit so far. The further improvement in methodology is considered to be the part of future works. Based on the results and considering the applicability of ICA so far, it can be concluded that a positive sign is observed for the improvement of mobility of gravity method.報告番号: ; 学位授与年月日: 2012-09-27 ; 学位の種別: 修士 ; 学位の種類: 修士（工学） ; 学位記番号: ; 研究科・専攻: 工学系研究科社会基盤学専

Semi-blind CFO estimation and ICA based equalization for wireless communication systems

In this thesis, a number of semi-blind structures are proposed for Orthogonal Frequency Division Multiplexing (OFDM) based wireless communication systems, with Carrier Frequency Offset (CFO) estimation and Independent Component Analysis (ICA) based equalization. In the first contribution, a semi-blind non-redundant single-user Multiple-Input Multiple-Output (MIMO) OFDM system is proposed, with a precoding aided CFO estimation approach and an ICA based equalization structure. A number of reference data sequences are carefully designed and selected from a pool of orthogonal sequences, killing two birds with one stone. On the one hand, the precoding based CFO estimation is performed by minimizing the sum cross-correlations between the CFO compensated signals and the rest of the orthogonal sequences in the pool. On the other hand, the same reference data sequences enable the elimination of permutation and quadrant ambiguities in the ICA equalized signals. Simulation results show that the proposed semi-blind MIMO OFDM system can achieve a Bit Error Rate (BER) performance close to the ideal case with perfect Channel State Information (CSI) and no CFO. In the second contribution, a low-complexity semi-blind structure, with a multi-CFO estimation method and an ICA based equalization scheme, is proposed for multiuser Coordinated Multi-Point (CoMP) OFDM systems. A short pilot is carefully designed offline for each user and has a two-fold advantage. On the one hand, using the pilot structure, a complex multi-dimensional search for multiple CFOs is divided into a number of low-complexity mono-dimensional searches. On the other hand, the cross-correlation between the transmitted and received pilots is explored to allow the simultaneous elimination of permutation and quadrant ambiguities in the ICA equalized signals. Simulation results show that the proposed semi-blind CoMP OFDM system can provide a BER performance close to the ideal case with perfect CSI and no CFO. In the third contribution, a semi-blind structure is proposed for Carrier Aggregation (CA) based CoMP Orthogonal Frequency Division Multiple Access (OFDMA) systems, with an ICA based joint Inter-Carrier Interference (ICI) mitigation and equalization scheme. The CFO-induced ICI is mitigated implicitly via ICA based equalization, without introducing feedback overhead for CFO correction. The permutation and quadrant ambiguities in the ICA equalized signals can be eliminated by a small number of pilots. Simulation results show that with a low training overhead, the proposed semi-blind equalization scheme can provide a BER performance close to the ideal case with perfect CSI and no CFO

Hybrid methods based on empirical mode decomposition for non-invasive fetal heart rate monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy(ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.Web of Science8512185120

Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

Background: Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in neuroscience. They allow for the unique opportunity to record brain activity with high spatial and temporal resolution. A common pre-processing choice in stereotactic EEG (S-EEG) is to re-reference the data with a bipolar montage. In this, each channel is subtracted from its neighbor, to reduce commonalities between channels and isolate activity that is spatially confined. New Method: We challenge the assumption that bipolar reference effectively performs this task. To extract local activity, the distribution of the signal source of interest, interfering distant signals, and noise need to be considered. Referencing schemes with fixed coefficients can decrease the signal to noise ratio (SNR) of the data, they can lead to mislocalization of activity and consequently to misinterpretation of results. We propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. Results: We describe and demonstrate this on human S-EEG recordings. In a simulation with real data, we quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when revealing local time series from the superposition of neighboring channels. Comparison with Existing Method: We argue that ICA already performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and will identify activity that is local. Conclusions: When investigating local sources in human S-EEG, ICA should be preferred over re-referencing the data with a bipolar montage