Erk Subasi

ETH Zurich, Zürich, ZH, Switzerland

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Publications (2)6.91 Total impact

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    ABSTRACT: Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.
    Journal of Neuroscience 10/2011; 31(40):14386-98. · 6.91 Impact Factor
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    ABSTRACT: In the past decade the field of neural interface systems has enjoyed an increase in attention from the scientific community and the general public, in part due to the enormous potential that such systems have to increase the quality of life for paralyzed patients. While significant progress has been made, serious challenges remain to be addressed from both biological and engineering perspectives. A key issue is how to optimize the decoding of neural information, such that neural signals are correctly mapped to effectors that interact with the outside world - like robotic hands and limbs or the patient's own muscles. Here we present some recent progress on tackling this problem by applying the latest developments in machine learning. Neural data was collected from macaque monkeys performing a real-time hand grasp decoding task. Signals were recorded via chronically implanted electrodes in the anterior intraparietal cortex (AIP) and ventral premotor cortex (F5), brain areas that are known to be involved in the transformation of visual signals into hand grasping instructions. We present a comparative study of different classical machine learning methods with an application of decoding of hand postures, as well as a new approach for more robust decoding. Results suggests that combining data-driven algorithmic approaches with well-known parametric methods could lead to better performing and more robust learners, which may have direct implications for future clinical devices.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:4172-5.