arXiv subjects

Carsten Mehring

Publications and source records attributed to Carsten Mehring.

8 recordsLinked to original sources

arXiv2024

Peripheral brain interfacing: Reading high-frequency brain signals from the output of the nervous system

Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. However, technologies used to directly measure CNS activity are limited by their resolution, sensitivity to interferences, and invasiveness. Advances in muscle recordings and deep learning allow us to decode the spiking activity of spinal motor neurons (MNs) in real time and with high accuracy. MNs represent the motor output layer of the CNS, receiving and sampling signals originating in different regions in the nervous system, and generating the neural commands that control muscles. The input signals to MNs can be estimated from the MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some neural activity from the CNS that reaches the MNs but does not directly modulate force production. We also discuss the evidence supporting this concept, and the necessary advances to consolidate and test MN-based CNS interfaces in controlled and real-world settings.

q-bio.NC↗

arXiv2024

Turbulent Accelerating Combusting Flows with a Methane-Vitiated Air Flamelet Model

This work presents a numerical study of a diffusion flame in a reacting, two-dimensional, turbulent, viscous, multi-component, compressible mixing layer subject to a large favorable streamwise pressure gradient. The boundary-layer equations are solved coupled with both the $k$-$\omega$ and SST turbulence models. A compressible extension of the flamelet progress variable method has been proposed and tested for use with large eddy simulations or Reynolds-averaged Navier-Stokes analyses of the burning of methane in pure air and vitiated air; the latter being particularly relevant in turbine burner scenarios. Effects of the level of detail of the reaction mechanism on the sub-grid and resolved-scale computations are studied. A comparison is made with results obtained using a simplified one-step reaction. The numerical results employing the flamelet model with the more detailed reaction mechanism show faster chemistry, significantly reduced peak temperatures and stronger sensitivity to pressure. Vitiated air flames are found to be dominated by unstable solutions, resulting in a weak flame with substantially lower peak temperature and impeded development, struggling to persist without quenching.

physics.flu-dyn↗

arXiv2021

Human movement augmentation and how to make it a reality

Augmenting the body with artificial limbs controlled concurrently to the natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this vision possible. By allowing individuals to achieve otherwise impossible actions, this movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with their environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning, and propose a vision for the field.

cs.RO↗

arXiv2016

Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface

To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands.

q-bio.NC↗

arXiv2016

Natural movement with concurrent brain-computer interface control induces persistent dissociation of neural activity

As Brain-computer interface (BCI) technology develops it is likely it may be incorporated into protocols that complement and supplement existing movements of the user. Two possible scenarios for such a control could be: the increasing interest to control artificial supernumerary prosthetics, or in cases following brain injury where BCI can be incorporated alongside residual movements to recover ability. In this study we explore the extent to which the human motor cortex is able to concurrently control movements via a BCI and overtly executed movements. Crucially both movement types are driven from the same cortical site. With this we aim to dissociate the activity at this cortical site from the movements being made and instead allow the representation and control for the BCI to develop alongside motor cortex activity. We investigated both BCI performance and its effect on the movement evoked potentials originally associated with overt execution.

q-bio.NC↗

arXiv2015

Inference based method for realignment of single trial neuronal responses

Neuronal responses to sensory stimuli or neuronal responses related to behaviour are often extracted by averaging neuronal activity over large number of experimental trials. Such trial-averaging is carried out to reduce noise and to reduce the influence of other signals unrelated to the corresponding stimulus or behaviour. However, if the recorded neuronal responses are jittered in time with respect to the corresponding stimulus or behaviour, averaging over trials may distort the estimation of the underlying neuronal response. Here, we present an algorithm, named dTAV algorithm, for realigning the recorded neuronal activity to an arbitrary internal trigger. Using simulated data, we show that the dTAV algorithm can reduce the jitter of neuronal responses for signal to noise ratios of 0.2 or higher, i.e. in cases where the standard deviation of the noise is up to five times larger than the neuronal response amplitude. By removing the jitter and, therefore, enabling more accurate estimation of neuronal responses, the dTAV algorithm can improve analysis and interpretation of the responses and improve the accuracy of systems relaying on asynchronous detection of events from neuronal recordings.

q-bio.NC↗

arXiv2014

Motor skill learning by increasing the movement planning horizon

We investigated motor skill learning using a path tracking task, where human subjects had to track various curved paths as fast as possible, in the absence of any external perturbations. Subjects became better with practice, producing faster and smoother movements even when tracking novel untrained paths. Using a "searchlight" paradigm, where only a short segment of the path ahead of the cursor was shown, we found that subjects with a higher tracking skill took a longer chunk of the future path into account when computing the control policy for the upcoming movement segment. We observed the same effects in a second experiment where tracking speed was fixed and subjects were practicing to increase their accuracy. These findings demonstrate that human subjects increase their planning horizon when acquiring a motor skill.

q-bio.NC↗

arXiv2012

Unsupervised adaptation of brain machine interface decoders

The performance of neural decoders can degrade over time due to nonstationarities in the relationship between neuronal activity and behavior. In this case, brain-machine interfaces (BMI) require adaptation of their decoders to maintain high performance across time. One way to achieve this is by use of periodical calibration phases, during which the BMI system (or an external human demonstrator) instructs the user to perform certain movements or behaviors. This approach has two disadvantages: (i) calibration phases interrupt the autonomous operation of the BMI and (ii) between two calibration phases the BMI performance might not be stable but continuously decrease. A better alternative would be that the BMI decoder is able to continuously adapt in an unsupervised manner during autonomous BMI operation, i.e. without knowing the movement intentions of the user. In the present article, we present an efficient method for such unsupervised training of BMI systems for continuous movement control. The proposed method utilizes a cost function derived from neuronal recordings, which guides a learning algorithm to evaluate the decoding parameters. We verify the performance of our adaptive method by simulating a BMI user with an optimal feedback control model and its interaction with our adaptive BMI decoder. The simulation results show that the cost function and the algorithm yield fast and precise trajectories towards targets at random orientations on a 2-dimensional computer screen. For initially unknown and non-stationary tuning parameters, our unsupervised method is still able to generate precise trajectories and to keep its performance stable in the long term. The algorithm can optionally work also with neuronal error signals instead or in conjunction with the proposed unsupervised adaptation.

cs.LG↗