EMBEDDED IMPLEMENTATION OF EEG ANALYSIS USING INDEPENDENT COMPONENT APPROACH

Brain signals are important in diagnosing various disorders and abnormalities in the human body. These signals are recorded by scalp electrodes and are called as EEG signals. EEG signals are a mixture of signals from different brain regions which contain artefacts along with original information. These contaminated mixtures are analysed such that diagnosis of various diseases is possible. One of the effective methods available is Independent Component Analysis (ICA) for removing artefacts and for separation and analysis of the desired sources from within the EEGs. This paper focuses on the analysis of EEG signals using ICA approach. Two ICA algorithms- Pearson ICA and JADE ICA are analysed in this paper. Comparison of these ICA algorithms in removing artefacts from EEG has been carried out by simulation using MATLAB. Then the Pearson ICA algorithm simulation is done using Visual C#. The algorithm has been implemented in an Embedded Development Kit (EDK) using .NET Micro Framework and the results are presented

Non-negative Tensor Factorization for Single-Channel EEG Artifact Rejection

International audienceNew applications of Electroencephalographic recording (EEG) pose new challenges in terms of artifact removal. In our work, we target informed source separation methods for artifact removal in single-channel EEG recordings by exploiting prior knowledge from auxiliary lightweight sensors capturing artifactual signals. To achieve this, we first propose a method using Non-negative Matrix Factorization (NMF) in a Gaussian source separation that proves competitive against the classic multi-channel Independent Component Analysis (ICA) technique. Additionally, we confront a probabilistic Non-negative Tensor Factorization (NTF) with ICA, both used in an original scheme that jointly processes the EEG and auxiliary signals. The adopted NTF strategy is shown to improve separation accuracy in comparison with the usual multi-channel ICA approach and the single EEG channel NMF method

Design of a Time-Frequency Algorithm for Automatic Eeg Artifact Removal

The injuries suffered by newborns during birth are a major health issue. To improve the health outcomes of sick newborns using EEG measurements, a number of recent studies focused on the use of high-resolution Time-Frequency Distributions to extract critical information from the collected signals [1]. Several algorithms have been proposed. A major problem in the implementation of such algorithms for fully automated EEG signal classification systems is caused by artifacts. In particular, previous studies have shown that a respiratory artifact looks like a seizure signal and can be misinterpreted by the automatic abnormality detection system thus resulting in false alarms. Hence, the successful removal of the artifacts is important, as shown in several previous studies [2]; and, there are two basic approaches for this: (1) use machine learning technique to detect and reject EEG segments corrupted by artifact; but this would result in the loss of EEG data [2]. (2) Correct EEG segments corrupted by artifacts; some artifacts can be corrected by a simple filter in a frequency domain, e.g. notch filter can be used to remove 50 Hz noise. This approach does not require any reference signals. For more complicated cases, when the spectrum of artifacts overlaps with the spectrum of EEG signals, blind source separation (BSS) algorithms can be used. Typically a multi-component EEG signal is transformed into a linear combination of independent components (that can be interpreted as channels (ICs)) by blind source separation techniques such as the independent component analysis (ICA) or canonical correlation analysis. The independent channels that are corrupted by artifacts are identified either manually or automatically using correlation information from a reference signal. The artifact free signal is then constructed by combining only artifact-free ICs.qscienc

Nonparametric Independent Component Analysis for the Sources with Mixed Spectra

Independent component analysis (ICA) is a blind source separation method to recover source signals of interest from their mixtures. Most existing ICA procedures assume independent sampling. Second-order-statistics-based source separation methods have been developed based on parametric time series models for the mixtures from the autocorrelated sources. However, the second-order-statistics-based methods cannot separate the sources accurately when the sources have temporal autocorrelations with mixed spectra. To address this issue, we propose a new ICA method by estimating spectral density functions and line spectra of the source signals using cubic splines and indicator functions, respectively. The mixed spectra and the mixing matrix are estimated by maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through simulation experiments and an EEG data application. The numerical results indicate that our approach outperforms existing ICA methods, including SOBI algorithms. In addition, we investigate the asymptotic behavior of the proposed method.Comment: 27 pages, 10 figure

Mixtures of independent component analyzers for EEG prediction

This paper presents a new application of independent component analysis mixture modeling (ICAMM) for prediction of electroencephalographic (EEG) signals. Demonstrations in prediction of missing EEG data in a working memory task using classic methods and an ICAMM-based algorithm are included. The performance of the methods is measured by using four error indicators: signal-to-interference (SIR) ratio, Kullback-Leibler divergence, correlation at lag zero and mean structural similarity index. The results show that the ICAMM-based algorithm outperforms the classical spherical splines method which is commonly used in EEG signal processing. Hence, the potential of using mixtures of independent component analyzers (ICAs) to improve prediction, as opposed on estimating only one ICA is demonstrated.This work has been supported by Generalitat Valenciana under grants PROMETEO/2010/040 and ISIC/2012/006Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Gonzalez, A.; Vidal Maciá, AM. (2012). Mixtures of independent component analyzers for EEG prediction. En Green and smart technology with sensor applications. Springer Verlag (Germany). 338:328-335. doi:10.1007/978-3-642-35251-5_46S328335338Common, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press, USA (2010)Salazar, A., Vergara, L., Serrano, A., Igual, J.: A general procedure for learning mixtures of independent component analyzers. Pattern Recognition 43(1), 69–85 (2010)Lee, T.W., Lewicki, M.S., Sejnowski, T.J.: ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1078–1089 (2000)Salazar, A., Vergara, L.: ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics. Eurasip Journal on Advances in Signal Processing 2010, article ID 125201, 11 pages (2010), doi:10.1155/2010/125201Klein, C., Feige, B.: An independent component analysis (ICA) approach to the study of developmental differences in the saccadic contingent negative variation. Biological Psychology 70, 105–114 (2005)Makeig, S., Westerfield, M., Jung, T.P., Covington, J., Townsend, J., Sejnowski, T.J., Courchesne, E.: Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention. Journal of Neuroscience 19(7), 2665–2680 (1999)Wibral, M., Turi, G., Linden, D.E.J., Kaiser, J., Bledowski, C.: Decomposition of working memory-related scalp ERPs: Crossvalidation of fMRI-constrained source analysis and ICA. Internt J. of Psychol. 67, 200–211 (2008)Castellanos, N.P., Makarov, V.A.: Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods 158, 300–312 (2006)Salazar, A., Vergara, L., Miralles, R.: On including sequential dependence in ICA mixture models. Signal Processing 90, 2314–2318 (2010)Dayan, P., Abbot, L.F.: Theoretical neuroscience: computational and mathematical modeling of neural systems. The MIT Press (2001)Sternberg, S.: High-speed scanning in human memory. Science 153(3736), 652–654 (1966)Raghavachari, S., Lisman, J.E., Tully, M., Madsen, J.R., Bromfield, E.B., Kahana, M.J.: Theta oscillations in human cortex during a working-memory task: evidence for local generators. J. of Neurophys. 95, 1630–1638 (2006)Gorriz, J.M., Puntonet, C.G., Salmeron, G., Lang, E.W.: Time series prediction using ICA algorithms. In: Proc. of 2nd IEEE Internat. W. on Intellig Data Acquisition and Advanc. Comp. Systems: Tech. and App., pp. 226–230 (2003)Lin, C.-T., Cheng, W.-C., Liang, S.-F.: An On-line ICA-Mixture-Model-Based Self-Constructing Fuzzy Neural Network. IEEE Transactions on Circuits and Systems I: Regular Papers 52(1), 207–221 (2005)Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended InfoMax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Computation 11(2), 417–441 (1999)Perrin, F., Pernier, J., Bertrand, D., Echallier, J.F.: Spherical splines for scalp potential and current density matching. Electroencep. and Clin. Neurophys. 72, 184–187 (1989)Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004

NOISE REMOVAL DUE TO MOTION ARTIFACT ON FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS

Technology in biomedical is growing all the time. We can now estimate our health condition just by analyzing the brain activity using brain-imaging methods such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The kinds of signals obtained from this device usually contain unwanted information signals or noises from various sources which need to be removed to obtain the original signal. In this thesis, we are analyzing fNIRS data obtained from physionet. As mentioned before, fNIRS is one of the brain-imaging methods which detects the brain activity by looking at the changes of oxy-Hemoglobin (oxy-Hb) and deoxy-Hemoglobin (deoxy-Hb) levels. This thesis proposes a method to detect and remove noise using fast independent component analysis (FastICA). FastICA is a popular algorithm of independent component analysis (ICA) which is one of the methods for blind source separation. We use this method since we are going to separate the noise from the noisy or contaminated signal, hence achieving the clean signal. The fNIRS data we have gathered contain the contaminated (noisy) signals and the clean signals from nine trials. This thesis expects the accuracy of the FastICA method to detect and remove noise. To analyze the performances of the method, we are calculate the value of mean square error (MSE), peak to signal noise ratio (PSNR) and cross-correlation from the results of the denoised signals

Improvement of EEG based brain computer interface by application of tripolar electrodes and independent component analysis

For persons with severe disabilities, a brain computer interface (BCI) may be a viable means of communication, with scalp-recorded electroencephalogram (EEG) being the most common signal employed in the operation of a BCI. Various electrode configurations can be used for EEG recording, one of which was a set of concentric rings that was referred to as a Laplacian electrode. It has been shown that Lapalacian EEG could improve classification in EEG recognition, but the complete advantages of this configuration have not been established. This project included two parts. First, a modeling study was performed using Independent Component Analysis (ICA) to prove that tripolar electrodes could provide better EEG signal for BCI. Next, human experiments were performed to study the application of tripolar electrodes in a BCI model to show that the application of tripolar electrodes and data-segment related parameter selection can improve EEG classification ratio for BCI. In the first part of work, an improved four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes. Four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements and talking. Finally, a fast fixed-point algorithm was used for signal-independent component analysis (ICA). The results showed that signals from tripolar electrodes generated better ICA separation than signals from disc electrodes for EEG signals, suggesting that tripolar electrodes could provide better EEG signal for BCI. The human experiments were divided into three parts: improvement of the data acquirement system by application of tripolar concentric electrodes and related circuit; development of pre-feature selection algorithm to improve BCI EEG signal classification; and an autoregressive (AR) model and Mahalanobis distance-based linear classifier for BCI classification. In the work, tripolar electrodes and corresponding data acquisition system were developed. Two sets of left/right hand motor imagery EEG signals were acquired. Then the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. The pre-feature selection methods were developed and applied to four data segment-related parameters: the length of the data segment in each trial (LDS), its starting position (SPD), the number of trials (NT) and the AR model order (AR Order). The study showed that, compared to the classification ratio (CR) without parameter selection, the CR was significantly different with an increase by 20% to 30% with proper selection of these data-segment-related parameter values and that the optimum parameter values were subject-dependent, which suggests that the data-segment-related parameters should be individualized when building models for BCI. The experiments also showed that that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes

Characterization of Neuroimage Coupling Between EEG and FMRI Using Within-Subject Joint Independent Component Analysis

The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing the relationship between EEG and fMRI within healthy subjects, it allows for comparison with a diseased population, and may offer ways to detect some of these conditions earlier. The correspondence between fMRI and EEG was first examined, and a methodological approach which was capable of informing to what degree the fMRI and EEG sources corresponded to each other was developed. Once it was certain that the EEG activity observed corresponded to the fMRI activity collected a methodological approach was developed to characterize the coupling between fMRI and EEG. Finally, this dissertation addresses the question of whether the use of jICA to perform this analysis increases the sensitivity to subcortical sources to determine to what degree subcortical sources should be taken into consideration for future studies. This dissertation was the first to propose a way to characterize the relationship between fMRI and EEG signals using blind source separation. Additionally, it was the first to show that jICA significantly improves the detection of subcortical activity, particularly in the case when both physiological noise and a cortical source are present. This new knowledge can be used to design studies to investigate subcortical signals, as well as to begin characterizing the relationship between fMRI and EEG across various task conditions

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

Independent component approach to the analysis of EEG and MEG recordings

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoen- cephalographic (MEG) recordings. In addition, ICA has been ap- plied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field