| EUROPEAN COMMISSION
Research Executive Agency
Marie Curie Actions – International Research Staff Exchange
|Funding||European Comission - 7th Framework Programme for Research, technological Development and Demonstration - Marie Curie Actions - IRSES. FP7-MC-IRSES|
|Project coordinator||Dr. Pierrick Legrand|
|Objective||Our aim was to develop new search and optimization strategies, based on evolutionary computation (EC) and genetic programming (GP) for the automatic induction of efficient and accurate classifiers. EC and GP are search techniques that can reach good solutions in highly multi-modal, non-differentiable and discontinuous spaces; and such is the case for the problem addressed here: the detection of mental states of vigilance. This project combines the expertise of researcher partners from five converging fields: Classification, Neurosciences, Signal Processing, Evolutionary Computation and Parallel Computing, in Europe - France (Inria, University of Bordeaux), Portugal (BioISI) and Spain (UNEX) - and North America - Mexico (ITT and CICESE). The partners complement and enhance their respective disciplines, allowing for a strong multi-disciplinary collaboration and proposal development. The exchange program goals and milestones gave a comprehensive strategy for the strengthening of current scientific relations amongst the partners, and allowed the construction of long-lasting scientific relationships that produced high quality theoretical and applied research.|
Over the last decade, Human-Computer Interaction (HCI) has grown and matured as a field. Gone are the days when only a mouse and keyboard could be used to interact with a computer. The most ambitious of such interfaces are Brain-Computer Interaction (BCI) systems. BCI's goal is to allow a person to interact with an artificial system using brain activity. A common approach towards BCI is to analyze, categorize and interpret Electroencephalography (EEG) signals in such a way that they alter the state of a computer. ACoBSEC's objective is to study the development of computer systems for the automatic analysis and classification of mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is required to be in a particular state. This problem is not a trivial one. In fact, EEG signals are known to be noisy, irregular and tend to vary from person to person, making the development of general techniques a very difficult scientific endeavor. Our aim is to develop new search and optimization strategies, based on evolutionary computation (EC) and genetic programming (GP) for the automatic induction of efficient and accurate classifiers. EC and GP are search techniques that can reach good solutions in multi-modal, non-differentiable and discontinuous spaces; and such is the case for the problem addressed here. This project combines the expertise of research partners from five converging fields:
Classification, Neurosciences (University of Bordeaux), Signal Processing, Evolutionary Computation and Parallel Computing in Europe (France Inria, Portugal FCUL, Spain UNEX) and South America (Mexico ITT, CICESE). The exchange program goals and milestones give a comprehensive strategy for the strengthening of current scientific relations amongst partners, as well as for the construction of long-lasting scientific relationships that produce high quality theoretical and applied research.
|Description of work packages||
WP1: Classification with Evolutionary Computation
The objective was to develop EC/GP-based classifiers for noisy real world data that exploit the advantages of statistical based approaches while also encouraging the explorative search capabilities of meta-heuristic based search strategies. It is now obvious that in order to solve difficult problems, hybrid approaches such as this could leverage the main strengths of each domain and possibly minimize some of their main weaknesses. Such algorithms have been useful in the particular sense of this project, where biological data and signals are well-known for their high levels of noise, heterogeneous frequency content, chaotic nature and dynamical properties. However, in a broader sense, developing such algorithms facilitated the use of hyper-heuristic based inductive learning in other real-world domain based on the strong cooperation of INRIA, FCUL and ITT.
WP2: Acquisition and study of Bio-Signals
From the discovery of the brain electrical activity and the invention of the electroencephalography recording technique (Berger 1929), the oscillatory rhythms of EEG have been correlated to different vigilance states. Those rhythms, defined by their frequency band, are still often used in diagnostics of pathologies. The use of EEG signal is evolving and is more efficient thanks to the joint improvement of the acquisition methods and signal analysis methods. This WP is directly related to the acquisition methods of EEG and to the study of the analysis method of these signals.
WP3: Analysis and feature extraction from EEG recordings of human states of vigilance
In this WP, the goal was to propose, develop and test several different types of feature extraction methods for EEG-based analysis and interpretation of human mental states of vigilance. The proposal was to take a diverse approach, incorporating different types of formal analysis, allowing us to compare results and evaluate the strengths and weaknesses of each method. Moreover, the proposal includes a heuristic approach, automatic feature synthesis through a GP-based search. It is assumed that no single feature can give a global solution to the problem of discriminately describing EEG signals, therefore hybrid features were proposed, composed of the above mentioned features combined in a principled manner using Fuzzy Logic. Finally, the quality of each proposed feature has been validated using a large scale experimental verification exploiting a distributed grid computing model.
WP4: Detection and Classification of human states of vigilance through EEG data
Almost all of the partners contributed in this WP, since most of the work developed in other WPs is used and tested here; including EEG recording methods, GP based classifiers and feature extractors.
WP5: Problem analysis and prediction of expected performance
Genetic Programming (GP) deals with the development of evolutionary algorithms (EA's) for automatic program induction , However, as for every EA, GP systems are stochastic search process, with many degrees of freedom and heuristic components. Therefore, as of yet, it is not possible to derive, from first principles, weather GP can solve a particular problem or task. A current goal within the GP community is to estimate how hard a problem instance might be for a specific GP. Such a measure could allow researchers to correctly choose and tune a GP search without the need of actually executing the code, which usually is computationally expensive. If we want to measure the difficulty of a problem in GP, we can consider at least two different frames of reference. The first is the problem domain, which is independent of the method used to solve the problem . The second frame of reference is to consider a perspective directly related to the process used to find a solution; in the case of GP this frame of reference corresponds with the search space and fitness landscape.
WP6: Modelling Neurological Behaviors
The goal of this WP was to derive descriptive models of the object of study, the dynamics that govern the evolution of mental states of vigilance over a series of prescribed stimuli. Given the detailed analysis of how to characterize and discriminate among different mental states, a detailed description of the dynamics of state transitions induced by external stimuli can be of great relevance for future study.
WP7: Tools integration in GPLAB
The goal of this WP was to assemble the tools developed in the other WPs into GPLAB. GPLAB is a Genetic Programming toolbox for MATLAB initially created and currently developed by Sara Silva. Most of its functions are used as "plug and play" devices, making it a versatile and easily extendable tool. The main objective of WP7 is to provide to the community the numerical products of our collaboration. A parallel objective is to extend and improve GPLAB with the new developed functionalities.