Neuroscience suggests that task characteristics (e.g., type and complexity of a movement) and subject-specific factors such as initial skill level, mood, age, or cognitive functions (e.g., attention, working memory, or engagement) play a fundamental role in motor learning. Ensuring an optimal balance between subject-specific factors and task demands during training facilitates motor learning and prevents patients’ frustration and boredom. Importantly, keeping the balance between cognitive functions and task demands is especially important to enhance neurorehabilitation outcomes since most neurologic patients present long-lasting cognitive impairments.
To open a discussion and find innovative solutions to optimize rehabilitation interventions, the Motor Learning and Neurorehabilitation (MLN) lab co-organized (together with Prof. Eduardo Rocon, from the Neural and Cognitive Engineering group, UPM, Spain) a special session at the International Conference in Neurorehabilitation 2020. In this special session, speakers from industry, clinics, and academy presented and discussed how emerging technologies such as Brain-Machine Interfaces (BMI) can be exploited to adapt current training protocols in order to optimize motor learning and neurorehabilitation. Further, the invited contributions showcased how neural implants or non-invasive neural recordings can be employed to investigate and treat neurologic conditions such as stroke or cerebellar ataxia.
Joaquin Penalver-Andres presented the latest results of his Ph.D. thesis titled: “EEG-based assessment and adaptation of robotic neurorehabilitation therapies to patients’ motivational and attentional needs”. The project is led by Prof. Marchal-Crespo and Dr. Karin Buetler from the ARTORG Center, together with clinical partners Prof. Dr. med. Müri and Prof. König from Inselspital and University Psychiatric Services (UPD). In a study with 36 healthy participants, the team investigated if task instructions enforcing the underlying task rule of a virtual sailing task modulate attentional engagement and motor learning. Attentional engagement was quantified using brain recordings (electroencephalography). The researchers observed that enforcing the rule of a motor task using explicit knowledge or visual cues enhances motor learning compared with no enforcement of task rules. Additionally, the team found that training with visual cues could support participants’ visual attention. Therefore, training parameters such as task instructions modulate the attentional status during motor learning and are an important factor to consider in neurorehabilitation.
ICNR2020 / Special Session / MLN contribution
Motor Learning and Neurorehabilitation: Neural Correlates of Motor Learning
