PACE Technics and Methods

PACE Research Programme encompasses a wide range of technical expertise, exposing PACE PhD fellows to a wide range of methods and technics, including:


Data acquisition

Behaviour Analysis
Hand completing a multiple choice exam.
Human participants make simple choice responses to controlled stimuli presented via a computer monitor, headphones or virtual reality devices. Experimental sessions typically last between 30 and 60 minutes, with stimulus intensities being within the normal range of these (consumer approved) devices. Many partners will implement psychophysics experiments.

Partners involved: CNRS-INT & ENS, VU, UB, TNO
Eye movement recording

Szakkad_wikicommonsEye position is monitored using a camera that picks up the reflection of a low intensity infra-red light source.

Partners involved: CNRS-INT, VU, UB, UCL, TNO
Arm/body movements recording

water-830374_640_pixabayMovements will be recorded either by 3D cameras or exoskeletons. Cameras will monitor the positions of small and light markers placed on the participant’s body. Exoskeletons consist in a rigid cast placed over the subject’s arms. The angular excursions of the cast (typically measured at the shoulder and the elbow joint) allow to monitor the arm movement.

Partners involved: CNRS-INT, VU, UB, TNO
Imaging Techniques
Electroencephalography (EEG)
The EEG technique consists in placing an array of sensors over the participant’s head to passively record signals arising from neuronal activity. EEG is a safe and non-invasive technique that has been used extensively for over 40 years. EEG will only be employed on healthy adults.

Partners involved: INT, BGU, Sheba and TNO
Magnetoelectroencephalography (MEG)

NIMH_MEGMEG is a technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Applications of MEG include basic research into perceptual and cognitive brain processes, localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain. MEG is a safe, non-invasive technique that has been used extensively in diagnostic radiology and in research.

Partners involved: WTCN and BGU
Magnetic resonance imaging (MRI)

526px-MRI-PhilipsMRI is a medical imaging technique used in radiology to investigate the anatomy andphysiology of the body in both health and disease. MRI scanners use magnetic fields and radio waves to form images of the body. MRI is a safe, non-invasive technique that has been used extensively for over 20 years in diagnostic radiology and for the past 10 years in research. MRI will be mainly employed on healthy young adults and in a minor proportion on elderly healthy subjects (at BGU).

Partners involved: INT, UDWTCN and BGU
Transcranial magnetic stimulation (TMS) and Transcranial direct current stimulation (tDCS)

Transcranial_magnetic_stimulationTMS is a noninvasive method used to stimulate small regions of the brain. During a TMS procedure, a magnetic field generator, or “coil” is placed near the head of the person receiving the treatment. The coil produces small electric currents in the region of the brain just under the coil via electromagnetic induction. The coil is connected to a pulse generator, or stimulator, that delivers electric current to the coil. TMS is a safe and non-invasive technique that has been used extensively for over 20 years. TMS is painless, and requires no anaesthesia. Importantly we do not plan to use repetitive TMS (rTMS), a version of TMS that may induce longer-lasting effects by means of trains of stimulation at high frequency . For all TMS experiments, only healthy young adults will be tested. 

tDCS is a non-invasive technique of neuro-stimulation that uses constant, low current delivered directly to the brain via small electrodes. It is painless, and requires no anaesthesia. This technique was originally developed to help patients with brain injuries such as strokes. Tests on healthy adults demonstrated that tDCS can increase cognitive performance on a variety of tasks (ranging from cognitive to motor tasks), depending on the area of the brain being stimulated.

Partners involved: INT (TMS) and BGU (tDCS)

Creation of Complex Environments

Virtual reality (VR)

9523_mb_file_4f6ff_smallVirtual Reality (VR) can be referred to as immersive multimedia or computer-simulated life. VR replicates an environment that simulates physical presence in places in the real world or imagined worlds. VR can recreate sensory experiences, which include virtual taste, sight, smell, sound, and touch. Participants to PACE VR experiences will view wide-field stimuli (projection screen or head mounted display) whilst their movements (finger, hand, head or body) will be tracked to update the visual or haptic stimuli.

Partners involved: SHEBA (Treadmill devices), IIT , Motek and UB (Haptic devices)

Romeo_robot_assistanceRobotics is the branch of mechanical engineering, electrical engineering and computer science that deals with the design, construction, operation, and application of robots,[1] as well as computer systems for their control, sensory feedback, and information processing.

Partners involved: IIT

Analysis and theoretical models

Probabilistic inference

BayesProbabilities are an universal mathematical language to describe information and how it transformed. In particular this theory allows to represent explicitly beliefs (for instance “Will it be sunny today?”) and their precision (“how sure am I about it?”). It thus plays an essential role in our understanding of the computations that are involved in perception and action, but also into dysfunctions of the cognitive system. The PACE network involves partner to confront their results with such theories and in particular with an embodied theory of probabilistic inference —active inference— developed by Prof Karl Friston (partner UCL).

Partners involved: UCL, CNRS-INT
Neural modelling

KremkowFig3Understanding normal et pathological mechanisms of perception and action involve making testable hypothesis on their neural substrate. Neural modeling within the PACE network consists in implementing large-scale neural networks and to challenge predictions made at the experimental, but also at the theoretical level. In particular, we are interested in bridging different levels of description from abstract functions to their neural substrate.

Partners involved: CNRS-INT