Person: Vidaurre Arbizu, Carmen
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Vidaurre Arbizu
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Carmen
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Ingeniería Eléctrica y Electrónica
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0000-0003-3740-049X
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2475
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Publication Open Access A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity(Plos one, 2019) Sannelli, Claudia; Vidaurre Arbizu, Carmen; Müller, Klaus Robert; Blankertz, Benjamin; Matemáticas; MatematikaBrain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.Publication Open Access Immediate brain plasticity after one hour of brain-computer interface (BCI)(Wiley, 2019) Nierhaus, Till; Vidaurre Arbizu, Carmen; Sannelli, Claudia; Müller, Klaus Robert; Villringer, Arno; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaA brain-computer-interface (BCI) allows humans to control computational devices using only neural signals. However, it is still an open question, whether performing BCI also impacts on the brain itself, i.e. whether brain plasticity is induced. Here, we show rapid and spatially specific signs of brain plasticity measured with functional and structural MRI after only 1 h of purely mental BCI training in BCI-naive subjects. We employed two BCI approaches with neurofeedback based on (i) modulations of EEG rhythms by motor imagery (MI-BCI) or (ii) event-related potentials elicited by visually targeting flashing letters (ERP-BCI). Before and after the BCI session we performed structural and functional MRI. For both BCI approaches we found increased T1-weighted MR signal in the grey matter of the respective target brain regions, such as occipital/parietal areas after ERP-BCI and precuneus and sensorimotor regions after MI-BCI. The latter also showed increased functional connectivity and higher task-evoked BOLD activity in the same areas. Our results demonstrate for the first time that BCI by means of targeted neurofeedback rapidly impacts on MRI measures of brain structure and function. The spatial specificity of BCI-induced brain plasticity promises therapeutic interventions tailored to individual functional deficits, for example in patients after stroke.Publication Embargo Enhancing sensorimotor BCI performance with assistive afferent activity: an online evaluation(Elsevier, 2019) Vidaurre Arbizu, Carmen; Ramos Murguialday, Ander; Haufe, Stefan; Gómez Fernández, Marisol; Müller, Klaus Robert; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaAn important goal in Brain-Computer Interfacing (BCI) is tofind and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MIor BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. Thesefinding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).Publication Open Access Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance(Frontiers Media, 2020) Vidaurre Arbizu, Carmen; Haufe, Stefan; Jorajuria Gómez, Tania; Müller, Klaus Robert; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaBrain-computer interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining 'good' and 'poor' BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.Publication Open Access Ensembles of adaptive spatial filters increase BCI performance: an online evaluation(IOP, 2016) Sannelli, Claudia; Vidaurre Arbizu, Carmen; Müller, Klaus Robert; Blankertz, Benjamin; Matemáticas; MatematikaObjective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.Publication Embargo Canonical maximization of coherence: a novel tool for investigation of neuronal interactions between two datasets(Elsevier, 2019) Vidaurre Arbizu, Carmen; Nolte, Guido; Vries, I. E. J. de; Gómez Fernández, Marisol; Boonstra, Tjeerd W.; Müller, Klaus Robert; Villringer, Arno; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaSynchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing syn-chronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG(electromyographic)/LFP (localfield potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. Thisallows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.Publication Open Access Improving motor imagery classification during induced motor perturbations(IOP Publishing, 2021) Vidaurre Arbizu, Carmen; Jorajuria Gómez, Tania; Ramos Murguialday, Ander; Müller, Klaus Robert; Gómez Fernández, Marisol; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaObjective. Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements. Approach. We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop. Main results. When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances. Significance. We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.Publication Open Access Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings(Elsevier, 2023) Vidaurre Arbizu, Carmen; Gurunandan, Kshipra; Jamshidi Idaji, Mina; Nolte, Guido; Gómez Fernández, Marisol; Villringer, Arno; Müller, Klaus Robert; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaInstantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.