Section: New Results

From the Mesoscopic to the Macroscopic Scale

Participants: Laurent Bougrain, Axel Hutt, Tamara Tošić, Cecilia Lindig-León, Romain Orhand, Sébastien Rimbert, Oleksii Avilov, Rahaf Al-Chwa.

In collaboration with Stéphanie Fleck (Univ. Lorraine)

In most BCI experimental paradigms based on Motor Imagery (MI), subjects perform continuous motor imagery (CMI), i.e., a repetitive and prolonged intention of movement, for a few seconds. To improve efficiency such as detecting faster a motor imagery and thus avoid fatigue and boredom, we proposed to show the difference between discrete motor imagery (DMI), i.e., a single short MI, and CMI. The results of the experiment involving 13 healthy subjects suggest that DMI generates a robust post-MI event-related synchronization (ERS). Moreover event-related desynchronization (ERD) produced by DMI seems less variable in certain cases compared to CMI [10], [12]. We showed the difference, in term of classification, between a DMI and a CMI. The results of the experiment involving 16 healthy subjects show that a BCI based on DMI is as effective as a BCI based on CMI and could be used to allow a faster detection [6].

The most common approach for classification consists of analyzing the signal during the course of the motor task within a frequency range including the alpha band, which attempts to detect the Event-Related Desynchronization (ERD) characteristics of the physiological phenomenon. However, to discriminate right-hand KMI and left-hand KMI, this scheme can lead to poor results on subjects for which the lateralization is not significant enough. To solve this problem, we proposed to analyze the signal at the end of the motor imagery within a higher frequency range, which contains the Event-Related Synchronization (ERS). We showed that 6 out of 15 subjects have a higher classification rate after the KMI than during the KMI, due to a higher lateralization during this period. Thus, for this population we obtained a significant improvement of 13% in classification taking into account the users lateralization profile [9].

Combined motor imageries can be detected to deliver more commands in a Brain-Computer Interface for controlling a robotic arm. Nevertheless only a few systems use more than three motor imageries: right hand, left hand and feet. Combining them allows to get four additional commands. We presented an electrophysiological study to show that i) simple motor imageries have mainly an electrical modulation over the cortical area related the body part involved in the imagined movement and that ii) combined motor imageries reflect a superposition of the electrical activity of simple motor imageries. A shrinkage linear discriminant analysis has been used to test as a first step how a resting state and seven motor imageries can be detected. 11 healthy subjects participated in the experiment for which an intuitive assignment has been done to associate motor imageries and movements of the robotic arm with 7 degrees of freedom [2], [5].

Each year, several million of general anesthesia are realized in France. A recent study shows that, between 0.1-0.2 % of patients are victims of intraoperative awareness. This kind of awakening could cause post-traumatic syndromes for the patient. Unfortunately, today, no monitoring system is able to avoid the intraoperative awareness phenomenon. Interestingly, if there is no subject movement due to curare, an electroencephalographical study of the motor cortex can help to detect an intention of movement. The dynamic study of motor cerebral activity during general anesthesia is essential if we want to create a brain-computer interface adapted to the detection of intraoperative awareness. We wrote a clinical protocol to allow EEG data recording during general anesthesia with propofol. Then, the development of temporal analysis specific methods allows us to quantify patterns of desynchronization and synchronization phases observed in delta, alpha and beta frequency bands to prevent intraoperative awareness [8].