Introduction After a stroke, brain activation reorganisation and its evolution through rehabilitation is a subject of major interest (Jones, 2017). To evaluate the level of functional recovery of the upper limb (UL) after stroke, our team has developed functional motor tasks evaluating the i) use of the arm and trunk compensation during a reaching task, and ii) speed-accuracy trade-off with an index of performance (IP) in a circular steering task (Muller et al., in review). Two non-invasive neuroimaging methods allow for recording the task-related brain activation: i) functional near-infrared spectroscopy (fNIRS) based on haemodynamic response and ii) functional electroencephalography (fEEG) based on electrical activity. Simultaneous use of fNIRS-EEG could allow for a better spatiotemporal mapping of the brain activity during the task to unveil underlying mechanisms of functional UL recovery. Nevertheless, before studying stroke patients, we need first normative brain activation data during UL functional tasks in healthy adults. This study is being part of a larger project (ReArm, Muller et al., in review). Objective. To depict the activation patterns of the motor cortex in both hemispheres using combined fNIRS-EEG methods and their association with the motor performance during UL functional tasks. Methods Sixteen (25.6 ± 5.3 years; 7 men) out of the twenty planned participants have already been recruited to participate in a single testing session of functional UL motor tasks. First, participants realize a reaching arm task with a target in front of them in a free then in a forced arm use (trunk restrained) condition for 3 blocks of 20s (20s of rest) for each arm/condition. Hand movement and trunk compensation while reaching in both conditions are assessed using a Kinect 3D motion capture device (LSL-Kinect; 10.5281/zenodo.4300182). Then, participants perform an electronic circular steering task (LSL-Mouse; 10.5281/zenodo.4297675) displacing a mouse as fast as possible on a tablet within fixed boundaries of a circle projected on a screen. Three blocks of 20s (20s of rest) are used for each arm and IP is assessed in terms of speed-accuracy trade-off. Meanwhile, brain activation over the motor cortex in both hemispheres is recorded using EEG (8 channels, around C3 and C4, 10-20 system) and NIRS (8 optodes, around C2 and C1). Laterality index (LI) and level of activation of the motor cortices are analysed with NIRS signals and the Mu (8-13Hz) and Beta (15-25Hz) rhythms through EEG signals. Preliminary Results Preliminary data of 5 subjects showed i) the same arm use in free and forced conditions in the reaching task and ii) a similar IP in the circular steering task. NIRS-related LI results showed a greater contralateral brain activation to the active arm, in line with our previous work (Delorme et al., 2019). Regarding EEG results, Mu-Beta rhythm event-related desynchronization was seen mainly during active periods compared to the rest period, in agreement with Bartur et al. (2019). Conclusion We aim to describe the association between brain patterns and arm movements in functional tasks using easy to use and low-cost neuroimaging and kinematics methods. Based on this first healthy population data set, we will be able to compare values to those recorded in a stroke population (ReArm project). References Bartur et al. (2019) Clin Neurophysiol; Delorme et al (2019) Restor Neurol Neurosci; Jones (2017) Nat Rev Neurosci; Muller et al. (in review) Trials