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Publications associées (152)

Segment-level training of ANNs based on acoustic confidence measures for hybrid HMM/ANN Speech Recognition

We show that confidence measures estimated from local posterior probabilities can serve as objective functions for training ANNs in hybrid HMM based speech recognition systems. This leads to a segment-level training paradigm that overcomes the limitation o ...

IEEE2019

Fractal Analysis Of Brain Glial Cells. Fractal Dimension And Lacunarity

Maria-Alexandra Paun

Both calculated quantities in the paper, fractal dimension and lacunarity respectively, prove high precision as foreteller to classify the glial cells tissue in various brain morphologies. The importance of microglia in neurodegenerative diseases and the i ...

UNIV POLITEHNICA BUCHAREST, SCI BULL2019

A Self-Learning Methodology for Epileptic Seizure Detection with Minimally Supervised Edge Labeling

David Atienza Alonso, Amir Aminifar, Damián Pascual Ortiz

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortali ...

2019

A Self-Learning Methodology for Epileptic Seizure Detection with Minimally-Supervised Edge Labeling

David Atienza Alonso, Amir Aminifar, Damián Pascual Ortiz

IEEE2019

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Silvestro Micera

Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of ...

WILEY2019

Improving Children Speech Recognition through Feature Learning from Raw Speech Signal

Selen Hande Kabil, Subrahmanya Pavankumar Dubagunta

Children speech recognition based on short-term spectral features is a challenging task. One of the reasons is that children speech has high fundamental frequency that is comparable to formant frequency values. Furthermore, as children grow, their vocal ap ...

IEEE2019

Bioresorbable Endovascular Neural Interface for Minimally Invasive Neural Stimulation and Recording

Diego Ghezzi, Laura Ferlauto, Adele Fanelli

Recording and stimulation of brain circuits have been widely used to treat neurological disorders, such as epilepsy1 and Parkinson’s disease2. Current available techniques in this field commonly require invasive surgery potentially entailing both transient ...

2018

Configuration of electrical spinal cord stimulation through real-time processing of gait kinematics

Grégoire Courtine, Jocelyne Bloch, Eduardo Martin Moraud, Tomislav Milekovic, Marco Capogrosso, Pavel Musienko, Nikolaus Wenger, Fabien Bertrand Paul Wagner, Jérôme Gandar, Polina Shkorbatova, Natalia Pavlova

Epidural electrical stimulation (EES) of the spinal cord and real-time processing of gait kinematics are powerful methods for the study of locomotion and the improvement of motor control after injury or in neurological disorders. Here, we describe equipmen ...

2018

Brain-controlled modulation of spinal circuits improves recovery from spinal cord injury

Silvestro Micera, Grégoire Courtine, Marco Bonizzato, Polina Shkorbatova, Natalia Pavlova, Galyna Pidpruzhnykova

The delivery of brain-controlled neuromodulation therapies during motor rehabilitation may augment recovery from neurological disorders. To test this hypothesis, we conceived a brain-controlled neuromodulation therapy that combines the technical and practi ...

Nature Publishing Group2018

Neuromodulation in the restoration of function after spinal cord injury

Nicholas David James

Neuromodulation, the use of electrical interfaces to alter neuronal activity, has been successful as a treatment approach in several neurological disorders, including deep brain stimulation for Parkinson's disease and epidural spinal stimulation for chroni ...

2018

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