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

Robust artifactual independent component classification for BCI practitioners

Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, ZentrumDFG, 194657344, EXC 1086: BrainLinks-BrainTool

Modeling sparse connectivity between underlying brain sources for EEG/MEG

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure

Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications

Magnetoneurographic evaluation of peripheral nerve regeneration

When a peripheral nerve is reconstructed after it has been damaged. it is important to assess, in an early stage, whether the nerve is regenerating across the lesion. However, at present for this purpose an adequate method is not available. In this study short term changes in the proximal and distal segment of a transected and reconstructed peripheral nerve are evaluated using a new quantitative magnetic recording technique. For a general understanding, the anatomy and neurophysiology of peripheral nerves will be discussed in this introduction, followed by an overview of clinical aspects of peripheral nerve reconstruction and regeneration, and of the techniques used for evaluation of nerve regeneration

The Role of the Proximal Segment in Peripheral Nerve Regeneration

The peripheral nervous system is designed to connect the orchestrations of brain and spinal cord to the rest of the body. In addition, it connects the outside world and that same brain, gathering information from numerous sensory organs in our skin and from our other “senses”. Injury to a nerve subsequently results in impairment of function as well as impairment of that information gathering system. In order to restore the damage, a series of complex changes is triggered in the cell body and the axon, all aimed at restoring motor and sensory function. However, different parts of the peripheral nervous system have different responses to injury. It is possible to distinguish three different parts: The cell body, the proximal segment and the distal segment. Proximal to the lesion, in the cell body and the proximal segment, the aim is to reconnect the axon to its effector organ as soon as possible. Distal to the lesion everything is aimed at creating an environment that allows reconnection of axons to happen. In order to accomplish this, the nerve proceeds through a number of morphological and electrophysiological changes. Although maybe not directly obvious, those morphological transformations after injury are reflected in electrophysiological changes. Previous research demonstrated changes in peak-peak amplitude of compound nerve action signals in the proximal segment after nerve transection and reconstruction45−48. However, the mechanisms involved however, are still unclear. The aim of this thesis is to explore the changes in the proximal segment and to clarify possible modifications to the proximal segment influencing repair