Dejan Popovic

Publications of Dejan Popovic

• Cortical excitability changes following grasping exercise augmented with electrical stimulation.

  Authors: Gergely Barsi, Dejan Popovic, Ina Tarkka, Thomas Sinkjær, Michael Grey

  Experimental brain research. Experimentelle Hirnforschung. Experimentation cerebrale. 08/2008;

  Rehabilitation with augmented electrical stimulation can enhance functional recovery after stroke, and cortical plasticity may play a role in this process. The purpose of this study was to compareRehabilitation with augmented electrical stimulation can enhance functional recovery after stroke, and cortical plasticity may play a role in this process. The purpose of this study was to compare the effects of three training paradigms on cortical excitability in healthy subjects. Cortical excitability was evaluated by analysing the input-output relationship between transcranial magnetic stimulation intensity and motor evoked potentials (MEPs) from the flexor muscles of the fingers. The study was performed with 25 healthy volunteers who underwent 20-min simulated therapy sessions of: (1) functional electrical stimulation (FES) of the finger flexors and extensors, (2) voluntary movement (VOL) with sensory stimulation, and (3) therapeutic FES (TFES) where the electrical stimulation augmented voluntary activation. TFES training produced a significant increase in MEP magnitude throughout the stimulation range, suggesting an increase in cortical excitability. In contrast, neither the FES nor voluntary movement alone had such an effect. These results suggest that the combination of voluntary effort and FES has greater potential to induce plasticity in the motor cortex and that TFES might be a more effective approach in rehabilitation after stroke than FES or repetitive voluntary training alone.

• Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

  Authors: Aleksandra Vuckovic, Vlada Radivojevic, Andrew C N Chen, Dejan Popovic

  Medical engineering & physics. 07/2002; 24(5):349-60.

  We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series ofWe present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

• Three Machine Learning Techniques for Automatic Determination of Rules to Control Locomotion

  Authors: Slavica Jonic, Student Member, Tamara Jankovic, Vladimir Gajic, Dejan Popovic

  01/2001;

  Automatic prediction of gait events (e.g., heel contact, flat foot, initiation of the swing, etc.) and corresponding profiles of the activations of muscles is important for realtime control ofAutomatic prediction of gait events (e.g., heel contact, flat foot, initiation of the swing, etc.) and corresponding profiles of the activations of muscles is important for realtime control of locomotion. This paper presents three supervised machine learning (ML) techniques for prediction of the activation patterns of muscles and sensory data, based on the history of sensory data, for walking assisted by a functional electrical stimulation (FES). Those ML's are: 1) a multilayer perceptron with Levenberg--Marquardt modification of backpropagation learning algorithm; 2) an adaptive-network-based fuzzy inference system (ANFIS); and 3) a combination of an entropy minimization type of inductive learning (IL) technique and a radial basis function (RBF) type of artificial neural network with orthogonal least squares learning algorithm. Here we show the prediction of the activation of the knee flexor muscles and the knee joint angle for seven consecutive strides based on the history of the knee joint angle and the ground reaction forces. The data used for training and testing of ML's was obtained from a simulation of walking assisted with an FES system [39]. The ability of generating rules for an FES controller was selected as the most important criterion when comparing the ML's. Other criteria such as generalization of results, computational complexity, and learning rate were also considered. The minimal number of rules and the most explicit and comprehensible rules were obtained by ANFIS. The best generalization was obtained by the IL and RBF network. Index Terms---Adaptive-network-based fuzzy inference system (ANFIS), artificial neural networks (ANN's), functional electrical stimulation (FES), inductive learning (IL), multilayer perceptron, radial basis function (RBF) ANN, ...

• Automatic Synthesis of Synergies for Control of Reaching --- Hierarchical Clustering

  Authors: Milan Jovovic, Slavica Jonic, Dejan Popovic

  01/2001;

  In this paper we describe a novel method for determining synergies between joint motions in reaching movements by hierarchical clustering. A set of recorded elbow and shoulder trajectories is used inIn this paper we describe a novel method for determining synergies between joint motions in reaching movements by hierarchical clustering. A set of recorded elbow and shoulder trajectories is used in a learning algorithm to determine the relationships between angular velocities at elbow and shoulder joints. The learning algorithm is based on optimal criteria for obtaining the hierarchy of descriptions of movement trajectories. We show that this method finds complex synergism between optimal joint trajectories for a given set of data and angular velocities at the shoulder and elbow joints. Three other machine learning techniques (ML) are used for comparison with our method of hierarchical clustering of trajectories. These MLs are: (1) radial basis functions (RBF), (2) inductive learning (IL), and (3) adaptive-network-based fuzzy inference system (ANFIS). Better error characteristics were obtained using the method of hierarchical clustering in comparison with the other techniques. The advantage of the method of hierarchical clustering with respect to the other MLs is in integrating the spatial and temporal elements of reaching movements. Determination and analysis of spatio-temporal events of movement trajectories is a useful tool in designing control systems for functional electrical stimulation (FES) assisted manipulation. 1999 IPEM. Published by Elsevier Science Ltd. All rights reserved.

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