Tomislav Milekovic

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Brain-machine interface (BMI) devices make errors in decoding. Detecting these errors online from neuronal activity can improve BMI performance by modifying the decoding algorithm and by correcting the errors made. Here, we study the neuronal correlates of two different types of errors which can both be employed in BMI: (i) the execution error, due to(More)
A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements(More)
BACKGROUND Brain-machine interfaces (BMIs) can translate the neuronal activity underlying a user's movement intention into movements of an artificial effector. In spite of continuous improvements, errors in movement decoding are still a major problem of current BMI systems. If the difference between the decoded and intended movements becomes noticeable, it(More)
Reach and grasp kinematics are known to be encoded in the spiking activity of neuronal ensembles and in local field potentials (LFPs) recorded from primate motor cortex during movement planning and execution. However, little is known, especially in LFPs, about the encoding of kinetic parameters, such as forces exerted on the object during the same actions.(More)
Brain-computer interface (BCI) systems are being developed with the goal of restoring or replacing function for individuals with paralysis, locked-in syndrome or upper limb loss. To this end, BCI research aims to improve understanding of the electrophysiological signals that encode details or goals of movements imagined by individuals unable to move their(More)
Brain-computer interfaces (BCIs) require demanding numerical computations to transfer brain signals into control signals driving an external actuator. Increasing the computational performance of the BCI algorithms carrying out these calculations enables faster reaction to user inputs and allows using more demanding decoding algorithms. Here we introduce a(More)
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 diminish the influence of other signals unrelated to the corresponding stimulus or behaviour. However, if the recorded(More)
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(More)
Brain­computer interfaces (BCIs) aim at restoring lost motor functions of paralysed patients by using neuronal activity to control an external effector, like a computer, a robotic arm or a hand/arm prosthesis. Electrocorticography (ECoG), a recording technique using electrodes placed directly on the surface of the brain, provides neuronal signals at a(More)
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