Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

Vidaurre, C.,Nolte, G.,de Vries, I.E.J.,Gó,mez, M.,Boonstra, T.W.,Mü,ller, K.-R.,Villringer, A.,Nikulin, V.V. Elsevier 2019 NeuroImage Vol.201 No.-

<P><B>Abstract</B></P> <P>Synchronization 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 synchronization, 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 (local field 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. This allows 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.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed a novel multivariate method for the detection of neural synchronization. </LI> <LI> Canonical Coherence (caCOH) maximizes coherence between two datasets. </LI> <LI> caCOH was validated in simulations and real data. </LI> <LI> caCOH is applicable for diverse brain-brain or brain-periphery signals (EEG/MEG/LFP/EMG). </LI> </UL> </P>

Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

Vidaurre, C.,Ramos Murguialday, A.,Haufe, S.,Gó,mez, M.,Mü,ller, K.-R.,Nikulin, V.V. ACADEMIC PRESS 2019 NEUROIMAGE Vol.199 No.-

<P><B>Abstract</B></P> <P>An important goal in Brain-Computer Interfacing (BCI) is to find 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 MI or 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. These finding 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).</P> <P><B>Highlights</B></P> <P> <UL> <LI> Afferent stimulation (STM) in the calibration phase was used to enhance BCI performance. </LI> <LI> Concurrent motor imagery and STM had stronger modulation of sensorimotor oscillations. </LI> <LI> STM significantly improved BCI accuracy particularly for poorly performing subjects. </LI> <LI> Classifiers trained with STM can be successfully used online even without stimulation. </LI> <LI> These findings ease the practical applicability of STM-based BCI systems. </LI> </UL> </P>

EEG-based BCI for the linear control of an upper-limb neuroprosthesis

Vidaurre, C.,Klauer, C.,Schauer, T.,Ramos-Murguialday, A.,Muller, K.R. Butterworth-Heinemann 2016 Medical engineering & physics Vol.38 No.11

Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI-FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

Sannelli, Claudia,Vidaurre, Carmen,Mü,ller, Klaus-Robert,Blankertz, Benjamin IOP 2016 Journal of neural engineering Vol.13 No.4

<P> <I>Objective</I>: 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. <I>Approach</I>: 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. <I>Main results</I>: 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. <I>Significance</I>: 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.</P>

A mathematical model for the two-learners problem

Mü,ller, Jan Saputra,Vidaurre, Carmen,Schreuder, Martijn,Meinecke, Frank C,von Bü,nau, Paul,Mü,ller, Klaus-Robert IOP 2017 Journal of neural engineering Vol.14 No.3

<P> <I>Objective</I>. We present the first generic theoretical formulation of the co-adaptive learning problem and give a simple example of two interacting linear learning systems, a human and a machine. <I>Approach</I>. After the description of the training protocol of the two learning systems, we define a simple linear model where the two learning agents are coupled by a joint loss function. The simplicity of the model allows us to find learning rules for both human and machine that permit computing theoretical simulations. <I>Main results</I>. As seen in simulations, an astonishingly rich structure is found for this eco-system of learners. While the co-adaptive learners are shown to easily stall or get out of sync for some parameter settings, we can find a broad sweet spot of parameters where the learning system can converge quickly. It is defined by mid-range learning rates on the side of the learning machine, quite independent of the human in the loop. Despite its simplistic assumptions the theoretical study could be confirmed by a real-world experimental study where human and machine co-adapt to perform cursor control under distortion. Also in this practical setting the mid-range learning rates yield the best performance and behavioral ratings. <I>Significance</I>. The results presented in this mathematical study allow the computation of simple theoretical simulations and performance of real experimental paradigms. Additionally, they are nicely in line with previous results in the BCI literature.</P>

Molecular Identification of Adenocephalus pacificus (Cestoda) from Three Human Cases in Lima Province, Peru

Aarón Mondragón-Martínez,Rosa Martínez-Rojas,Enrique Garcia-Candela,Abraham Delgado-Escalante,Manuel Tantaleán-Vidaurre,Lidia Cruz-Neyra 대한기생충학ㆍ열대의학회 2020 The Korean Journal of Parasitology Vol.58 No.4

The Pacific tapeworm Adenocephalus pacifcus (syn. Diphyllobothrium pacificum) is a causative agent of diphyllobothriosis occurred in Pacific coast of South America, mainly in Peru. Source of infections are traditional meal from raw or undercooked marine fish such as “cebiche”. We confirmed 3 new cases, one including scolex and the other two headless. A strobila 46 cm long without scolex was discharged from an 8-year-old boy before treatment. Specimens were confirmed morphologically by presence of tegumental protuberances on proglottids and small sized eggs. Partial sequence of cytochrome c oxidase subunit 1 gene was congruent with A. pacificus sequences.