Vergleichende Untersuchungen zur Eignung eines neuen Oberflächenkonditionierungsverfahrens (Airsonic Mini Sandblaster®) in der Klebebrückentechnik / Comparative Studies on the Applicability of a New Surface Conditioning System (Airsonic Mini Sandblaster®) in Adhesive Bridging Technic

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification

Common spatial pattern (CSP) as a spatial filtering method has been most widely applied to electroencephalogram (EEG) feature extraction to classify motor imagery (MI) in brain-computer interface (BCI) applications. The effectiveness of CSP is determined by the quality of interception in a specific time window and frequency band. Although numerous algorithms have been designed to optimize CSP by splitting the EEG data with a sliding time window and dividing the frequency bands with a set of band-pass filters, simultaneously. However, they did not consider the drawbacks of the rapid increase in data volume and feature dimensions brought about by this method, which would reduce the classification accuracy and calculation efficiency of the model. Therefore, we propose an optimal channel-based sparse time-frequency blocks common spatial pattern (OCSB-CSP) feature extraction method to improve the model classification accuracy and computational efficiency. Comparative experiments on two public EEG datasets show that the proposed method can quickly select significant time-frequency blocks and improve classification performance. The average classification accuracies are higher than those of other winners' methods, providing a new idea for the improvement of BCI applications.

The Decision Decoding ToolBOX (DDTBOX) - A Multivariate Pattern Analysis Toolbox for Event-Related Potentials

In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig 2004; Lopez-Calderon and Luck 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches

A structural or functional pattern of neuroplasticity that could systematically discriminate between people with impaired and preserved motor performance could help us to understand the brain networks contributing to preservation or compensation of behavior in multiple sclerosis (MS). This study aimed to (1) investigate whether a machine learning-based technique could accurately classify MS participants into groups defined by upper extremity function (i.e. motor function preserved (MP) vs. motor function impaired (MI)) based on their regional grey matter measures (GMM, cortical thickness and deep grey matter volume) and inter-regional functional connection (FC), (2) investigate which features (GMM, FC, or GMM + FC) could classify groups more accurately, and (3) identify the multivariate patterns of GMM and FCs that are most discriminative between MP and MI participants, and between each of these groups and the healthy controls (HCs). With 26 MP, 25 MI, and 21 HCs (age and sex matched) underwent T1-weighted and resting-state functional MRI at 3 T, we applied support vector machine (SVM) based classification to learn discriminant functions indicating regions in which GMM or between which FCs were most discriminative between groups. This study demonstrates that there exist structural and FC patterns sufficient for correct classification of upper limb motor ability of people with MS. The classifier with GMM + FC features yielded the highest accuracy of 85.61 % (p < 0.001) to distinguish between the MS groups using leave-one-out cross-validation. It suggests that a machine-learning approach combining structural and functional features is useful for identifying the specific neural substrates that are necessary and sufficient to preserve motor function among people with MS.

The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential

OBJECTIVE

Movement control is an important application for EEG-BCI (EEG-based brain-computer interface) systems. A single-modality BCI cannot provide an efficient and natural control strategy, but a hybrid BCI system that combines two or more different tasks can effectively overcome the drawbacks encountered in single-modality BCI control.

APPROACH

In the current paper, we developed a new hybrid BCI system by combining MI (motor imagery) and mVEP (motion-onset visual evoked potential), aiming to realize the more efficient 2D movement control of a cursor.

MAIN RESULT

The offline analysis demonstrates that the hybrid BCI system proposed in this paper could evoke the desired MI and mVEP signal features simultaneously, and both are very close to those evoked in the single-modality BCI task. Furthermore, the online 2D movement control experiment reveals that the proposed hybrid BCI system could provide more efficient and natural control commands.

SIGNIFICANCE

The proposed hybrid BCI system is compensative to realize efficient 2D movement control for a practical online system, especially for those situations in which P300 stimuli are not suitable to be applied.

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface

Brain computer interfaces (BCIs), based on multi-channel electroencephalogram (EEG) signal processing convert brain signal activities to machine control commands. It provides new communication way with a computer by extracting electroencephalographic activity. This paper, deals with feature extraction and classification of horizontal mental task pattern on 1-D cursor movement from EEG signals. The hemispherical power changes are computed and compared on alpha & beta frequencies and horizontal cursor control extracted with only mental imagination of cursor movements. In the first stage, features are extracted with the well-known average signal power or power difference (alpha and beta) method. Principal component analysis is used for reducing feature dimensions. All features are classified and the mental task patterns are recognized by three neural network classifiers which learning vector quantization, multilayer neural network and probabilistic neural network due to obtaining acceptable good results and using successfully in pattern recognition via k-fold cross validation technique.

Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques

Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associated with functional impairment in "individual" patients. We investigated whether fMRI allows classification of individual motor impairment after stroke using support vector machines (SVMs). Forty acute stroke patients and 20 control subjects underwent resting-state fMRI. Half of the patients showed significant impairment in hand motor function. Resting-state connectivity was computed by means of whole-brain correlations of seed time-courses in ipsilesional primary motor cortex (M1). Lesion location was identified using diffusion-weighted images. These features were used for linear SVM classification of unseen patients with respect to motor impairment. SVM results were compared with conventional mass-univariate analyses. Resting-state connectivity classified patients with hand motor deficits compared with controls and nonimpaired patients with 82.6-87.6% accuracy. Classification was driven by reduced interhemispheric M1 connectivity and enhanced connectivity between ipsilesional M1 and premotor areas. In contrast, lesion location provided only 50% sensitivity to classify impaired patients. Hence, resting-state fMRI reflects behavioral deficits more accurately than structural MRI. In conclusion, multivariate fMRI analyses offer the potential to serve as markers for endophenotypes of functional impairment.

Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces

Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.

Distinct dynamical patterns that distinguish willed and forced actions

The neural pathways for generating willed actions have been increasingly investigated since the famous pioneering work by Benjamin Libet on the nature of free will. To better understand what differentiates the brain states underlying willed and forced behaviours, we performed a study of chosen and forced actions over a binary choice scenario. Magnetoencephalography recordings were obtained from six subjects during a simple task in which the subject presses a button with the left or right finger in response to a cue that either (1) specifies the finger with which the button should be pressed or (2) instructs the subject to press a button with a finger of their own choosing. Three independent analyses were performed to investigate the dynamical patterns of neural activity supporting willed and forced behaviours during the preparatory period preceding a button press. Each analysis offered similar findings in the temporal and spatial domains and in particular, a high accuracy in the classification of single trials was obtained around 200 ms after cue presentation with an overall average of 82%. During this period, the majority of the discriminatory power comes from differential neural processes observed bilaterally in the parietal lobes, as well as some differences in occipital and temporal lobes, suggesting a contribution of these regions to willed and forced behaviours.

The brain-computer interface cycle

Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

A review of classification algorithms for EEG-based brain–computer interfaces

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

Classification of movement intention by spatially filtered electromagnetic inverse solutions

We couple standardized low-resolution electromagnetic tomography, an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (brain-computer interface) competition 2003, data set IV. The data set is from an experiment where a subject performed a self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represent uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and our extraction of only one attribute per class, our method yields accuracy similar to the winners of the competition for this data set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers.

Multi-channel linear descriptors for event-related EEG collected in brain computer interface

By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

Towards adaptive classification for BCI

Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.