Motor imagery | EPFL Graph Search

Motor imagery is a mental process by which an individual rehearses or simulates a given action. It is widely used in sport training as mental practice of action, neurological rehabilitation, and has also been employed as a research paradigm in cognitive neuroscience and cognitive psychology to investigate the content and the structure of covert processes (i.e., unconscious) that precede the execution of action. In some medical, musical, and athletic contexts, when paired with physical rehearsal, mental rehearsal can be as effective as pure physical rehearsal (practice) of an action. Definition Motor imagery can be defined as a dynamic state during which an individual mentally simulates a physical action. This type of phenomenal experience implies that the subject feels themselves performing the action. It corresponds to the so-called internal imagery (or first person perspective) of sport psychologists. Mental practice of action Mental practice refers to use of visuo

Motor state transitions and Breathing in Brain Machine Interfaces

Bastien Orset

Brain-Machine interfaces aim to create a direct neural link between user's brain and machines. This goal has pushed scientists to investigate a large spectrum of applications in the realm of assistive and rehabilitation technologies. However, despite great progress, the possibility of being in full control of a device with our brain is still far to be reached. One of the primary limitations of BMIs is the notion of self-paced control, which states that a user should be able to activate or deactivate such interface by mere modulation of brain activity. Many studies have investigated this question, focusing notably on the use of endogenous signals (such as Event-Related Desynchronization/Synchronization, ERD/S) to control the activation of these interfaces in natural interaction scenarios. This question is, however, largely ignored when looking at their interruption. Hence, in this first part of the thesis, my work was devoted to investigate how a BMI user would be able to control the interruption of non-invasive BMIs based on Motor Imagery (MI), a paradigm promoting natural and endogenous signals that can be used for BMI control. I investigated the decoding of motor termination as well as its correlates characterized by the post-movement ERS phenomenon. Specifically, this part of the thesis aimed to study (i) the feasibility to decode motor termination from a specific neural correlate as well as the adaptation from the user in closed-loop scenarios, (ii) the benefits of using motor termination correlates to detect the stopping process, and (iii) the effect of BMI effectors such as an exoskeleton on the detection of motor termination correlates. The obtained results provide new insights into closed-loop decoding of motor termination. In particular, I show that BMI users exhibit an adaptation of their EEG correlates enabling them to have a reliable control when switching off a BMI in closed-loop scenarios. Second, I show that the decoding of motor termination is a particular process different from a resting state and, hence, should be decoded independently so as to achieve a faster and more robust detection. Finally, I investigate the use of BMI effectors with respect to the decoding of motor termination showing an effect of the effectors on the correlates of motor termination. However, due to the nature of these correlates, motor termination can still be reliably decoded with a similar latency. In the second part of my thesis, I also investigate how the interoceptive system affects BMI and particularly how breathing signals affect BMI based on MI. Breathing has been shown to have a key effect on numerous human functions, including, perceptual, cognitive and motor functions. However, currently not much is known about the role of respiratory signals in BMI and such signals have rather been considered as physiological noise. In my thesis, I evaluated how the breathing process affects the correlates of MI (ERD in mu and beta bands) as well as actual BMI performance. The results provide an extensive analysis regarding the effect of the breathing cycle specifically on mu-ERDs, showing stronger ERD during the late expiration phase. Moreover, I identified a link between breathing and BMI performance and propose that breathing signals are a valuable predictor for BMI performance highlighting the importance of monitoring such signals and, more generally, present the interoceptive system as a key component of motor preparation and motor imagery

EPFL2021

Device for a Novel Hand and Wrist Rehabilitation Strategy for Stroke Patients Based on Illusory Movements Induced by Tendon Vibration

Mike Domenik Rinderknecht

This paper presents a new device for a novel neurorehabilitation strategy for stroke patients suffering from upper limb paralysis. The concept consists in combining illusory movements, induced by tendon vibration on the hand and wrist, with a virtual reality visualization of the hand to support the positive effects of motor imagery reported in the literature. The aim is to induce and support plastic processes in affected brain regions to promote the ability to perform basic gestures like grasping. The device is capable of vibrating simultaneously or independently with an adjustable frequency on precise locations on the flexor tendons of the five fingers. The experimental results show that strong illusory movements can be induced in a group of 10 healthy subjects with a very high reliability of 86 to 100% demonstrating the concept feasibility and performance of the solution.

Ieee2012

Reliable decoding of motor state transitions during imagined movement

Ricardo Andres Chavarriaga Lozano, Bastien Orset

Current non-invasive Brain Machine interfaces commonly rely on the decoding of sustained motor imagery activity. This approach enables a user to control brain-actuated devices by triggering predetermined motor actions. However, despite of its broad range of applications, this paradigm has failed so far to allow a natural and reliable control. As an alternative approach, we investigated the decoding of states transitions of an imagined movement, i.e. rest-to-movement (onset) and movement-to-rest (offset). We show that both transitions can be reliably decoded with accuracies of 71.47% for the onset and 73.31% for the offset (N = 9 subjects). Importantly, these transitions exhibit different neural patterns and need to be decoded independently. Our results indicate that both decoders are able to capture the brain dynamics during imagined movements and that their combined use could provide benefits in terms of accuracy and time precision.

IEEE2019