chronic stroke

Brain-Machine Interfaces: Beyond Decoding

A brain-machine interface (BMI) is a system that enables users to interact with computers and robots through the voluntary modulation of their brain activity. Such a BMI is particularly relevant as an aid for patients with severe neuromuscular disabilities, although it also opens up new possibilities in human-machine interaction for able-bodied people. Real-time signal processing and decoding of brain signals are certainly at the heart of a BMI. Yet, this does not suffice for subjects to operate a brain-controlled device. In the first part of my talk I will review some of our recent studies, most involving participants with severe motor disabilities, that illustrate additional principles of a reliable BMI that enable users to operate different devices. In particular, I will show how an exclusive focus on machine learning is not necessarily the solution as it may not promote subject learning. This highlights the need for a comprehensive mutual learning methodology that foster learning at the three critical levels of the machine, subject and application. To further illustrate that BMI is more than just decoding, I will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, I will show how these principles translate to motor rehabilitation, where in a controlled trial chronic stroke patients achieved a significant functional recovery after the intervention, which was retained 6-12 months after the end of therapy.

EFFECTS OF COMBINING COGNITIVE AND TREADMILL TRAINING ON COGNITIVE FUNCTION AND WALKING PERFORMANCE IN CHRONIC STROKE PATIENT WITH MILD COGNITIVE IMPAIRMENT

FENS Forum 2026

MOVEMENT-INFORMED CLOSED-LOOP BETA FREQUENCY TACS IN CHRONIC STROKE SURVIVORS

FENS Forum 2026

INVESTIGATING THE DISTINCT SPATIOTEMPORAL ROLES OF INTERLEUKIN-1Α AND INTERLEUKIN-1Β DURING CHRONIC STROKE

FENS Forum 2026

Impaired reward processing in chronic stroke survivors

Robot-assisted voluntary shoulder rehabilitation training modifies synergetic muscle control in a chronic stroke patient: a case report