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Alexis Gkogkidis

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3 papers

2 author rows

YNIMG Journal 2018 Journal Article

Computationally optimized ECoG stimulation with local safety constraints

Seyhmus Guler
Moritz Dannhauer
Biel Roig-Solvas
Alexis Gkogkidis
Rob Macleod
Tonio Ball
Jeffrey G. Ojemann
Dana H. Brooks

Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice.

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ICRA Conference 2016 Conference Paper

Automatic bone parameter estimation for skeleton tracking in optical motion capture

Tobias Schubert 0002
Katharina Eggensperger
Alexis Gkogkidis
Frank Hutter
Tonio Ball
Wolfram Burgard

Motion analysis is important in a broad range of contexts, including animation, bio-mechanics, robotics and experiments investigating animal behavior. For applications, in which tracking accuracy is one of the main requirements, passive optical motion capture systems are widely used. Many skeleton tracking methods based on such systems use a predefined skeleton model, which is scaled once in the initialization step to the individual size of the character to be tracked. However, there are remarkable differences in the bone length relations across gender and even more across mammal races. In practice, the optimal skeleton model has to be determined in a manual and time-consuming process. In this paper, we reformulate this task as an optimization problem aiming to rescale a rough hierarchical skeleton structure to optimize probabilistic skeleton tracking performance. We solve this optimization problem by means of state-of-the-art blackbox optimization methods based on sequential model-based Bayesian optimization (SMBO). We compare different SMBO methods on three real-world datasets with an animal and humans, demonstrating that we can automatically find skeleton structures for previously unseen mammals. The same methods also allow an automated choice of a suitable starting frame for initializing tracking.

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ICRA Conference 2015 Conference Paper

Automatic initialization for skeleton tracking in optical motion capture

Tobias Schubert 0002
Alexis Gkogkidis
Tonio Ball
Wolfram Burgard

The ability to track skeletal movements is important in a variety of applications including animation, biological studies and animal experiments. To detect even small movements, such a method should provide highly accurate estimates. Besides that it should not impede the mammal in its motion. This motivates the usage of a passive optical motion capture system. Thereby the main challenges are the initialization, the association of the unlabeled markers to their corresponding segment also across the frames, and the estimation of the skeleton configuration. While many existing approaches can deal with the latter two problems, they typically need a specific pose for initialization. This is rather unpractical in the context of animal tracking and often requires a manual initialization process. In this paper, we present an approach to reliably track animals and humans in marker-based optical motion capture systems with freely attached markers. Our method is also able to perform an automatic initialization without any pre-or post-processing of the data. To achieve this, our approach utilizes a large database of previously observed poses. We present our algorithm and its evaluation on real-world data sets with an animal and humans. The results demonstrate that our initialization method performs accurately for the most kind of initial poses and our tracking approach outperforms a popular fully automatic skeleton tracking method especially with respect to the smoothness of the motion.

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