Section: New Results
Higher level functions
Participants : Frédéric Alexandre, Laurent Bougrain, Axel Hutt, Nanying Liang, Randa Kassab, Maxime Rio, Carolina Saavedra.
This year, our activities concerned information analysis and interpretation and the design of numerical distributed and adaptive algorithms in interaction with biology and medical science. To better understand cortical signals, we choose a top-down approach for which data analysis techniques extract properties of underlying neural activity. To this end several unsupervised methods and supervised methods are investigated and integrated to extract features in measured brain signals.
Template-based classifiers to detect evoked potentials
To detect efficiently transient events in multivariate time series, we develop pattern recognition techniques for graphic elements, e.g. event-related potential, auditory evoked potential, k-complex, sleep spindles or vertex waves, which are present in electroencephalographic signals [17] . More specifically, template-based classifiers have been proposed to robustly detect evoked potentials in a single trial from noisy and multi-sources electro-encephalographic (EEG) signals. In this context, we have extended the learning vector quantization (LVQ) algorithm by Kohonen to non-identity assignment to robustly detect evoked potentials in noisy electro-encephalographic (EEG) signals for brain-computer interfaces (BCIs). The improved LVQ is obtained by optimizing its assignment layer using the minimum-norm least-square algorithm, the same scheme used by extreme learning machine (ELM)(Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K., Extreme learning machine: A new learning scheme of feedforward neural networks, Proceedings of International Joint Conference on Neural Networks, Vol. 2, 985-990, Budapest, Hungary, (2004).). The proposed LVQ is evaluated using the Wadsworth P300 speller dataset from BCI competition III. The experimental results show that the proposed algorithm improves the accuracy with less computational units compared to original LVQ and ELM. Based on these results, an international STIC AmSud project started in 2009 on P300 single-trial detection (cf. § 8.4 ).
Decoding Finger Flexion from ECoG in Brain-Machine Interfaces (BMI)
Over the last two decades, major advances in both multi-electrode recording techniques and the development of decoding algorithms have provided new tools for brain-machine interfaces (BMIs). We developed data analysis techniques to extract properties of underlying neural activity from multi-electrode recordings for direct BMIs for the control of a skilled hand movement. We won the international BCI competition IV, datasets 4, on the prediction of individual finger flexion from electro-corticogram (ECoG) using amplitude modulation in specific bands. We built a linear decoding scheme based on bandspecific amplitude modulation with a window to the past for predicting finger flexion from ECoG signals. The sensitivity profile of ECoG is clearly band-specific. The gamma band (60-100Hz) seems to provide more useful information. Only a few features are useful. A half-second window through the past improves the prediction [28] , [22] .
Detection of synchronization in Local Field Potentials
The brain represents a network of brain areas whose interaction is still poorly understood. It is supposed that the interaction mechanism between these areas is based on the synchronization of the dendritic activities in the areas. Since Local Field Potentials (LFPs) reflect this activity, we focus on the study of LFPs obtained experimentally to better understand the inter-area information exchange. In collaboration with the Max Planck Institute for Biological Cybernetics, we investigate the synchronization of LFPs obtained intracranially from various monkey brain areas. The corresponding experiment combines visual attention and motor action and thus allows for the study of the visio-motor feedback loop. The data analysis [49] aims to detect time windows of increased phase synchronization between brain areas and relates these time windows to the monkey behavior.
Detection of event-related components in single trial EEG
In cognitive experiments, electroencephalograpic data (EEG) may be recorded to investigate the brain activity during cognition and to reveal the information processing pathways in the brain. Typically, the experimental task (one trial) is repeated many times and the resulting brain activity is averaged over trials. The main reason for this averaging is the low signal-to-noise ratio (SNR) in the single trials and average increases the SNR dramatically. The average activity allows to extract easily event-related components, which are strongly related to cognitive processes in the brain.
However, this averaging assumes that the brain responds to the external stimuli identically in all trials. However it has been shown in several previous studies that this assumption is not valid. Consequently, to improve the analysis we develop an algorithm to extract event-related components from single trials. This algorithm is part of the PhD-project of Maxime Rio. It is based on a Gaussian mixture model and is implemented in a Bayesian framework.
