Project-Team:CQFD

Project-Team Cqfd

Members

Overall Objectives

Presentation

Research Program

Application Domains

Dependability and safety

New Software and Platforms

New Results

Highlights of the Year
Approximate Kalman–Bucy filter for continuous-time semi-Markov jump linear systems
Modeling and optimization of a launcher integration process
Numerical approximation for optimal stopping of MDP under partial observation
Classification of EEG signals by evolutionary algorithm
Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms
Variable selection to construct indicators of quality of life for data structured in groups
Efficiency of simulation in monotone hyper-stable queueing networks
Control of parallel non-observable queues: asymptotic equivalence and optimality of periodic policies
The economics of the cloud: price competition and congestion
Generalized Nash Equilibria for Platform-as-a-Service Clouds
Stochastic approximations of constrained discounted Markov decision processes
Non-Parametric Estimation of the Conditional Distribution of the Interjumping Times for Piecewise-Deterministic Markov Processes
Approximation of average cost Markov decision processes using empirical distributions and concentration inequalities
Piecewise Deterministic Markov Processes based approach applied to an offshore oil production system
Optimal Trajectories for Underwater Vehicles by Quantization and Stochastic control
Multi-Objective Design and Maintenance Optimization of the Heated Hold-Up Tank Modeled by Piecewise Deterministic Markov Processes
Conditional quantile estimation through optimal quantization
Conditional quantile estimator based on optimal quantization: from theory to practice
QuantifQuantile : an R package for performing quantile regression trough optimal quantization
Transcriptome profile analysis reveals specific signatures of pollutants in Atlantic eels
Comparaison of kernel density estimators with assumption on number of modes : application on environmental monitoring data
A new sliced inverse regression method for multivariate response
An introduction to dimension reduction in nonparametric kernel regression
Hidden Markov Model for the detection of a degraded operating mode of optronic equipment
On the asymptotic behavior of the Nadaraya-Watson estimator associated with the recursive SIR method
Evolving Genetic Programming Classifiers with Novelty Search
Detecting mental states of alertness with genetic algorithm variable selection
A comparison of fitness-case sampling methods for Symbolic Regression
Geometric Semantic Genetic Programming with Local Search

Bilateral Contracts and Grants with Industry

Partnerships and Cooperations

Dissemination

Bibliography

Inria | Raweb 2014 | Presentation of the Project-Team CQFD


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Section: New Results

Classification of EEG signals by evolutionary algorithm

Participants : Marie Chavent, Pierrick Legrand, Leonardo Trujillo.

The goal of this work is to predict the state of alertness of an individual by analyzing the brain activity through electroencephalographic data (EEG) captured with 58 electrodes. Alertness is characterized here as a binary variable that can be in a "normal" or "relaxed" state. We collected data from 44 subjects before and after a relaxation practice, giving a total of 88 records. After a pre-processing step and data validation, we analyzed each record and discriminate the alertness states using our proposed "slope criterion". Afterwards, several common methods for supervised classification (k nearest neighbors, decision trees (CART), random forests, PLS and discriminant sparse PLS) were applied as predictors for the state of alertness of each subject. The proposed "slope criterion" was further refined using a genetic algorithm to select the most important EEG electrodes in terms of classification accuracy. Results show that the proposed strategy derives accurate predictive models of alertness.

This work has been published in a book chapter [45] .

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