Tomasz M. Rutkowski

Noninvasive BCIs: Multiway Signal-Processing Array Decompositions

In addition to helping better understand how the human brain works, the brain-computer interface neuroscience paradigm allows researchers to develop a new class of bioengineering control devices and robots, offering promise for rehabilitation and other medical applications as well as exploring possibilities for advanced human-computer interfaces.

EMD APPROACH TO MULTICHANNEL EEG DATA — THE AMPLITUDE AND PHASE COMPONENTS CLUSTERING ANALYSIS

Human brains exhibit a possibility to control directly the intelligent computing applications in form of brain computer/machine interfacing (BCI/BMI) technologies. Neurophysiological signals and especially electroencephalogram (EEG) are the forms of brain electrical activity which can be easily captured and utilized for BCI/BMI applications. Those signals are unfortunately usually very highly contaminated by external noise caused by the presence of different devices in the environment creating electromagnetic interference. In this paper, we first decompose each of the recorded channels, in multichannel EEG recording environment, into intrinsic mode functions (IMF) which are a result of empirical mode decomposition (EMD) extended to multichannel analysis. We present novel and interesting results on human mental and cognitive states estimation based on analysis of the above-mentioned stimuli-related IMF components. The IMF components are further clustered for their spectral similarity in order to identify only those carrying responses to present stimuli to the subjects. The resulting targets only reconstruction allows us to identify when and to which stimuli intelligent application user is tuning at a time.

Estimation of speech embedded in a reverberant and noisy environment by independent component analysis and wavelets

In this paper, we develop a system for enhancement of the speech signal with highest energy from a linear convolutive mixture of n statistically independent sound sources recorded by m microphones, where m<n. In this system we use the concept of independent component analysis (ICA) along with adaptive auditory filter banks and pitch tracking. Computer simulations and real-world experiments carried out in an actual room and measured through objective and subjective measures confirm the validity of the proposed algorithm.

Ocular Artifacts Removal from EEG Using EMD

Electroencephalogram (EEG) provides a non-invasive way to analyze brain activity. Blinking and movement of the eyes causes a strong electrical activity that can contaminate EEG recordings, particularly around the forehead but also as far as in occipital areas. Removal of such ocular artifacts is a considerable signal processing problem, since those artifacts overlap in frequency domain with EEG. In this paper we propose a signal reconstruction method based on a time frequency analysis tool known as the Hilbert-Huang spectrum. We demonstrate how our reconstruction scheme can be successfully applied to contaminated EEG data for the purposes of removing unwanted ocular artifacts.

Collaborative Adaptive Learning using Hybrid Filters

A novel stable and robust algorithm for training of finite impulse response adaptive filters is proposed. This is achieved based on a convex combination of the least mean square (LMS) and a recently proposed generalised normalised gradient descent (GNGD) algorithm. In this way, the desirable fast convergence and stability of GNGD is combined with the robustness and small steady state misadjustment of LMS. Simulations on linear and nonlinear signals in the prediction setting support the analysis.

Blind Source Separation and Sparse Bump Modelling of Time Frequency Representation of Eeg Signals: New Tools for Early Detection of Alzheimer&#8217;s Disease

The early detection of Alzheimers disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using only electroencephalographic (EEG) recordings for patients with mild cognitive impairment (MCI) without any clinical symptoms of the disease who later developed AD. In our method, first a blind source separation algorithm is applied to extract the most significant spatiotemporal uncorrelated components; afterward these components are wavelet transformed; subsequently the wavelets or more generally time frequency representation (TFR) is approximated with sparse bump modeling approach. Finally, reliable and discriminant features are selected and reduced with orthogonal forward regression and the random probe methods. The proposed features were finally fed to a simple neural network classifier. The presented method leads to a substantially improved performance (93% correctly classified - improved sensitivity and specificity) over classification results previously published on the same set of data. We hope that the new computational and machine learning tools provide some new insights in a wide range of clinical settings, both diagnostic and predictive

Separation of EOG artifacts from EEG signals using bivariate EMD

A problem of eye-movement muscular interference removal from EEG recordings is described. In many experiments in neuroscience it is crucial to separate different sources of electrical activity within human body in a situation when a very limited knowledge about nonlinear and nonstationary nature of the mixing process is available. A new two step extension to bivariate empirical mode decomposition is proposed to remove ocular artifacts from EEG with a use of fractional Gaussian noise as a reference first to preprocess EOG signal, which is next used in the second step as a reference to clean EEG signals. Results with EEG experimental data validate the proposed approach.

Constrained non-Negative Matrix Factorization Method for EEG Analysis in Early Detection of Alzheimer Disease

Approximate non-negative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in biomedical data analysis. In this paper, we proposed a new NMF algorithm with temporal smoothness constraint that aims to extract non-negative components that have meaningful physical or physiological interpretations. We propose two constraints and derive new multiplicative learning rules. Specifically, we apply the proposed algorithm, combined with advanced time-frequency analysis and machine learning techniques, to early detection of Alzheimer disease using clinical EEG recordings. Empirical results show promising performance

A blind extraction of temporally correlated but statistically dependent acoustic signals

We propose a batch learning algorithm for sequential blind extraction of arbitrary distributed but generally not i.i.d. (independent identically distributed) temporally correlated sources, possibly dependent speech signals from from a linear mixture. The proposed algorithm is computationally very simple and efficient, it is based only on second order statistics and in contrast to most known algorithms developed for sequential blind extraction and independent component analysis, do not assume statistical independence of source signals nor non-zero kurtosis for the sources, thus statistical dependent signals including sources with extremely low or even zero kurtosis (colored Gaussian with different spectra) can be also successfully extracted. Extensive computer simulations confirm the validity and high performance of the proposed algorithm.

Multichannel spectral pattern separation - An EEG processing application -

A problem of information separation in multichannel recordings is important in engineering applications such as brain computer/machine interfaces (BCI/BMI). Whereas this problem is not entirely new, engineering approaches connecting the mental states of humans and the observed electroencephalography (EEG) recordings are still in their infancy, mostly due to problems with electrophysiological denoising. The electrophysiological signals captured in form of the EEG carry brain activity in form of the neurophysiological components which are usually embedded in much higher power electrical muscle activity components (electromyography - EMG; electrooculography - EOG; etc.). In this paper we present an approach to remove muscular interference caused by eye-movements from EEG recorded during auditory experiments in an eight channel recording setting. This is achieved by analyzing the correlation of the oscillatory modes within a multichannel signal in the Hilbert domain. Simulations in a real world auditory BCI setting support the analysis.