Cheolsoo Park

Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.

The In-the-Ear Recording Concept: User-Centered and Wearable Brain Monitoring

The integration of brain monitoring based on electroencephalography (EEG) into everyday life has been hindered by the limited portability and long setup time of current wearable systems as well as by the invasiveness of implanted systems (e.g. intracranial EEG). We explore the potential to record EEG in the ear canal, leading to a discreet, unobtrusive, and user-centered approach to brain monitoring. The in-the-ear EEG (Ear-EEG) recording concept is tested using several standard EEG paradigms, benchmarked against standard onscalp EEG, and its feasibility proven. Such a system promises a number of advantages, including fixed electrode positions, user comfort, robustness to electromagnetic interference, feedback to the user, and ease of use. The Ear-EEG platform could also support additional biosensors, extending its reach beyond EEG to provide a powerful health-monitoring system for those applications that require long recording periods in a natural environment.

EMD VIA MEMD: MULTIVARIATE NOISE-AIDED COMPUTATION OF STANDARD EMD

A noise-assisted approach in conjunction with multivariate empirical mode decomposition (MEMD) algorithm is proposed for the computation of empirical mode decomposition (EMD), in order to produce localized frequency estimates at the accuracy level of instantaneous frequency. Despite many advantages of EMD, such as its data driven nature, a compact decomposition, and its inherent ability to process nonstationary data, it only caters for signals with a sufficient number of local extrema. In addition, EMD is prone to mode-mixing and is designed for univariate data. We show that the noise-assisted MEMD (NA-MEMD) approach, which utilizes the dyadic filter bank property of MEMD, provides a solution to the above problems when used to calculate standard EMD. The method is also shown to alleviate the effects of noise interference in univariate noise-assisted EMD algorithms which directly add noise to the data. The efficacy of the proposed method, in terms of improved frequency localization and reduced mode-mixing, is demonstrated via simulations on electroencephalogram (EEG) data sets, over two paradigms in brain-computer interface (BCI).

Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition

A novel method is introduced to determine asymmetry, the lateralization of brain activity, using extension of the algorithm empirical mode decomposition (EMD). The localized and adaptive nature of EMD make it highly suitable for estimating amplitude information across frequency for nonlinear and nonstationary data. Analysis illustrates how bivariate extension of EMD (BEMD) facilitates enhanced spectrum estimation for multichannel recordings that contain similar signal components, a realistic assumption in electroencephalography (EEG). It is shown how this property can be used to obtain a more accurate estimate of the marginalized spectrum, critical for the localized calculation of amplitude asymmetry in frequency. Simulations on synthetic data sets and feature estimation for a brain-computer interface (BCI) application are used to validate the proposed asymmetry estimation methodology.

Measuring phase synchrony using complex extensions of EMD

A framework for the robust assessment of phase synchrony between multichannel observations is introduced. This is achieved by using Empirical Mode Decomposition (EMD), a data driven technique which decomposes nonlinear and nonstationary data into their oscillatory components (scales). In general, it is rarely possible to jointly process two or more channels due to the non-uniqueness of the decompositions. To guarantee the same decomposition levels for every pair of channels analysed, we consider phase synchrony within the recently introduced framework of complex extensions of EMD. Simulation results on brain signals support the analysis.

The complex local mean decomposition

The local mean decomposition (LMD) has been recently developed for the analysis of time series which have nonlinearity and nonstationarity. The smoothed local mean of the LMD surpasses the cubic spline method used by the empirical mode decomposition (EMD) to extract amplitude and frequency modulated components. To process complex-valued data, we propose complex LMD, a natural and generic extension to the complex domain of the original LMD algorithm. It is shown that complex LMD extracts the frequency modulated rotation and envelope components. Simulations on both artificial and real-world complex-valued signals support the analysis.

Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks

A novel augmented complex-valued common spatial pattern (CSP) algorithm is introduced in order to cater for general complex signals with noncircular probability distributions. This is a typical case in multichannel electroencephalogram (EEG), due to the power difference or correlation between the data channels, yet current methods only cater for a very restrictive class of circular data. The proposed complex-valued CSP algorithms account for the generality of complex noncircular data, by virtue of the use of augmented complex statistics and the strong-uncorrelating transform (SUT). Depending on the degree of power difference of complex signals, the analysis and simulations show that the SUT based algorithm maximizes the inter-class difference between two motor imagery tasks. Simulations on both synthetic noncircular sources and motor imagery experiments using real-world EEG support the approach.

Geoacoustic inversion in time domain using ship of opportunity noise recorded on a horizontal towed array

A time domain geoacoustic inversion method using ship noise received on a towed horizontal array is presented. The received signal, containing ship noise as a source of opportunity, is time-reversed and then back-propagated to the vicinity of the ship. The back-propagated signal is correlated with the modeled signal which is expected to peak at the ships location in case of a good match for the environment. This match is utilized for the geoacoustic parameter inversion. The objective function for this optimization problem is thus defined as the normalized power focused in an area around the source position, using a matched impulse response filter. A hybrid use of global and local search algorithms, i.e., GA and Powells method is applied to the optimization problem. Applications of the proposed inversion method to MAPEX 2000 noise experiment conducted north of the island of Elba show promising results, and it is shown that the time domain inversion takes advantage of dominant frequencies of the source signature automatically.

EEG Gamma Band Oscillations Differentiate the Planning of Spatially Directed Movements of the Arm Versus Eye: Multivariate Empirical Mode Decomposition Analysis

The neural dynamics underlying the coordination of spatially-directed limb and eye movements in humans is not well understood. Part of the difficulty has been a lack of signal processing tools suitable for the analysis of nonstationary electroencephalographic (EEG) signals. Here, we use multivariate empirical mode decomposition (MEMD), a data-driven approach that does not employ predefined basis functions. High-density EEG, and arm and eye movements were synchronously recorded in 10 subjects performing time-constrained reaching and/or eye movements. Subjects were allowed to move both the hand and the eyes, only the hand, or only the eyes following a 500-700 ms delay interval where the hand and gaze remained on a central fixation cross. An additional condition involved a nonspatially-directed “lift” movement of the hand. The neural activity during a 500 ms delay interval was decomposed into intrinsic mode functions (IMFs) using MEMD. Classification analysis revealed that gamma band (30 Hz <;) IMFs produced more classifiable features differentiating the EEG according to the different upcoming movements. A benchmark test using conventional algorithms demonstrated that MEMD was the best algorithm for extracting oscillatory bands from EEG, yielding the best classification of the different movement conditions. The gamma rhythm decomposed using MEMD showed a higher correlation with the eventual movement accuracy than any other band rhythm and than any other algorithm.

Geoacoustic Inversion Using Backpropagation

This paper presents inversion results of the 2006 Shallow Water Experiment (SW06) data measured on a vertical line array. A low-frequency (100-900 Hz) chirp source was towed along two tracks (circle, straight line) at 30-m depth. For the inversions, a three-step optimization scheme is applied to the data using very fast simulated reannealing (VFSR). The objective function is defined by the energy of the backpropagated signal from the array to the source. At each step, water-column sound-speed profile (SSP), experimental geometry, and geoacoustic parameters are inverted successively. An environmental model is employed consisting of a linear segmented SSP in the water column, a sediment layer, and a half-space. The geometric parameter inversion results show good agreement with in situ measurements. Finally, the estimated geoacoustic parameters show that the experimental site near the vertical line array (VLA) is fairly homogeneous in bottom properties consisting of a 21-m-thick sediment layer with sound speed of around 1600 m/s over a hard basement whose sound speed is approximately 1750 m/s.