Sylvain Chevallier

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Attentional focusing can be implemented with a neural field [1], which uses a discharge rate code. As an alternative, we propose in the present work an implementation based on spiking neurons. Such implementation will allow to investigate the possible contribution of a spike time based code with a network of leaky integrate-and-fire neurons. The network is(More)
The deep learning paradigm tackles problems on which shallow architectures (e.g. SVM) are affected by the curse of dimensionality. As part of a two-stage learning scheme involving multiple layers of nonlinear processing a set of statistically robust features is automatically extracted from the data. The present tutorial introducing the ESANN deep learning(More)
Common spatial pattern (CSP) is widely used for constructing spatial filters to extract features for motor-imagery-based BCI. One main parameter in CSP-based classification is the number of spatial filters used. An automatic method relying on Rayleigh quotient is presented to estimate its optimal value for each subject. Based on an existing dataset, we(More)
We present a distributed spiking neuron network (SNN) for handling low-level visual perception in order to extract salient locations in robot camera images. We describe a new method which reduce the computional load of the whole system, stemming from our choices of architecture. We also describe a modeling of post-synaptic potential, which allows to quickly(More)
Built from a need for modelling cognitive processes, a modular neural network is designed as the “brain” of a virtual robot moving in a prey-predator environment. The robot decides its path from the animals it identifies around. Both a parallel implementation of distributed processes and a temporal coding of spiking neurons allow the robot to develop(More)
Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the(More)
Neural communication generates oscillations of electric potential in the extracellular medium. In feedback, these oscillations affect the electrochemical processes within the neurons, influencing the timing and the number of action potentials. It is unclear whether this influence should be considered only as noise or it has some functional role in neural(More)
Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI(More)