Section: New Results

From the Mesoscopic to the Macroscopic Scale

Participants: Laurent Bougrain, Axel Hutt, Pedro Garcia-Rodriguez, Eric Nichols, Guillaume Serrière, Tamara Tosic, Mariia Fedotenkova, Meysam Hashemi, Benjamin le Golvan, Cecilia Lindig-Leon, Sébastian Rimbert.

Level of Consciousness

Spatio-temporal Dynamics in Neural Fields

Neural fields serve as a model for experimental macroscopic activity. We have developed a numerical simulator NeuralFieldSimulator [21] . In addition, we have worked out a neural neural field model that exhibits a sequence of metastable activity states as observed in experimental data [4] .

Synchronisation in Local Field Potentials under Anaesthesia

We have applied advanced data analysis techniques based on wavelet analysis to detect instantaneous partial synchronisation in experimental data [5] .

Statistical Frequency-dependent Analysis by Recurrence Plots

Participants : Axel Hutt, Mariia Fedotenkova, Tamara Tosic

In collaboration with Flavio Frohlich, Peter Beim Graben and Kristin K. Sellers

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system’s dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. We proposed a methodology to extract recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands [6] . Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the delta-band activity is much less recurrent than alpha-band activity. Moreover, alpha-activity is susceptible to pre-stimuli, while delta-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.

Motor System

Participants: Laurent Bougrain, Axel Hutt, Benjamin le Golvan, Cecilia Lindig-Leon, Sébastian Rimbert, Guillaume Serrière

Motor Patterns during General Anesthesia

Participants: Laurent Bougrain, Axel Hutt, Cecilia Lindig-Leon, Sébastian Rimbert, Guillaume Serrière

The dosage of the anesthetic agent is tricky: too low, it does not achieve a sufficient loss of consciousness and may lead to a partial memorization during surgery and a post-operative trauma; too strong, it is dangerous for people with respiratory or heart problems. To better monitor the effect of the current dosage, we propose to study the dynamics of the motor brain activity during anesthesia. The relationship between motor brain activity and anesthesia is not intensively studied. Yet even if no physical movement by the patient is visually detectable, an electroencephalographic analysis of brain activity in motor areas may reveal an intention movement. This information is important because it demonstrates that the patient is conscious. We started to define a clinical protocol in collaboration with anesthesiologists of the hospital in Nancy to investigate is possibility. To reduce the duration of the protocol, we studied the minimum duration of a motor imagery to allow its detection from EEG recordings [23] . A large number of Brain-Computer Interfaces (BCIs) are based on the detection of motor imagery related features in the electroencephalographic signal. In most BCI experimental paradigms, subjects realize continuous motor imagery, i.e. a prolonged intention of movement, during a time window of a few seconds. Then, the system detects the movement based on the event-related desynchronization (ERD) and the event-related synchronization (ERS) principles. We studied if a discrete motor imagery, corresponding to a single short motor imagery, would allow a better detection of ERD and ERS patterns than a continuous motor imagery. Indeed, the results of experiments involving 11 healthy subjects suggest that a continuous motor imagery generates a later ERS as well as a more variable and less detectable ERD than discrete motor imagery [11] . This finding suggests an improved experimental paradigm. We deeper investigated the amplitude and latency of EEG Beta activity during real movements, discrete and continuous motor imageries [22] .

Motor Patterns during Combined Movements

Participants: Laurent Bougrain, Cecilia Lindig-Leon

Imaginary motor tasks cause brain oscillations that can be detected through the analysis of electroencephalo-graphic (EEG) recordings. We studied whether or not the characteristics of the brain activity induced by the combined motor imagery (MI) of both hands can be assumed as the superposition of the activity generated during simple hand MIs. After analyzing the sensorimotor rhythms in EEG signals of five healthy subjects, results show that the imagination of both hands movement generates in each brain hemisphere similar activity as the one produced by each simple hand MI in the contralateral side [8] . Furthermore, during simple hand MIs, brain activity over the ipsilateral hemisphere presents similar characteristics as those observed during the rest condition. Thus, it is shown that the proposed scheme is valid and promising for brain-computer interfaces (BCI) control, allowing to easily detect patterns induced by combined MIs. Based on these results, we proposed a new method to extend the classic Common Spatial Pattern (CSP) algorithm to a multi-class approach which analyses both brain hemispheres separately to solve, together with a stepwise classification strategy, a multi-label BCI problem. After testing the proposed approach over the EEG signals of six healthy subjects performing a four-class multi-label task involving simple and combined hand MIs together with the rest condition, results show that this technique is plausible for BCI control [7] . In terms of accuracy, it outperforms the classical one-vs-one approach by 20% and has the same performance as the one-vs-all method. Nevertheless, to solve a multi-label classification problem involving k classes, the proposed method requires only log2(k) classifiers, whereas the one-vs-one method uses k(k-1)/2 classifiers and the one-vs-all k classifiers, thereby the new approach simplifies the classification task and seems promising for solving multi-label problems involving numerous classes.

On-line Detection of the End of Motor Imageries

Participants : Cécilia Lindig-León, Laurent Bougrain and Sébastien Rimbert

Limb movement execution or imagination induce sensorimotor rhythms that can be detected in electroencephalographic (EEG) recordings. We presented the interest of considering not only the beta frequency band but also the alpha band to detect the elicited EEG rebound, i.e. the increasing of oscillatory power synchronization, at the end of motor imageries [9] , [19] . This phenomenon can be stronger over the alpha than the beta band and it is experimentally demonstrated [9] that the analysis on the alpha band improves the detection of the end of motor imageries. Moreover a variant method to compute the oscillatory power without referring to a baseline period is proposed; such capacity is useful for self-paced BCI control.

Pain under General Anaesthesia

Detection of EEG-signal Features for Pain under General Anaesthesia

Participants : Axel Hutt, Mariia Fedotenkova

In collaboration with Peter Beim Graben and James W. Sleigh

Nowadays, surgical operations are impossible to imagine without general anaesthesia, which involves loss of consciousness, immobility, amnesia and analgesia. Understanding mechanisms underlying each of these effects guarantees well-controlled medical treatment. Our work focuses on analgesia effect of general anaesthesia, more specifically, on patients reaction on nociception stimuli. The study was conducted on dataset consisting of 230 EEG signals: pre- and post-incisional recordings for 115 patients, who received desflurane and propofol. Initial analysis was performed by power spectral analysis, which is a widespread approach in signal processing. Power spectral information was described by fitting the background activity and measuring power contained in delta and alpha bands according to power of background activity. The fact that power spectrum of background activity decays as frequency increasing is well known and thoroughly studied. Here, traditional $1/f^{\alpha }$ behaviour of the decay was replaced by a Lorentzian model to describe the power spectrum of background activity. Due to observed non-stationary nature of EEG signals spectral analysis does not suffice to reveal significant changes between two states. A further improvement was done by expanding spectra with time information. To obtain time-frequency representations of the signals conventional spectrograms were used as well as a spectrogram reassignment technique. The latter allows to ameliorate readability of a spectrogram by reassigning energy contained in spectrogram to more precise positions. Subsequently, obtained spectrograms were used in recurrence analysis and its quantification by complexity measure. Recurrence analysis allows to describe and visualise dynamics of a system and discover structural patterns contained in the data. Structure of each recurrence plot is characterised by Lempel–Ziv complexity measure [5], which shows a difference between pre- and post-incision [13] .