| Description |
Monitoring workload and fatigue using wearable sensors might be very valuable in several conditions involved during every day life, and even more impact in more specific applications requiring attention during prolonged period of time (e.g. during the surgical act performed by a surgeon, during a mission accomplished by an astronaut or a soldier). In this context, on can take benefit of light and portable devices, to measure bioelectrical neuronal activity with Electro-EncephaloGraphy (EEG) together with cortical hemodynamic processes with Near InfraRed Spectroscopy NIRS. EEG measures on the scalp bioelectrical discharges elicited by synchronous neuronal populations, whereas NIRS monitors brain hemodynamic processes, exploiting the complementary absorption properties of oxy-hemoglobin (HbO) and deoxyhemoglobin (HbR), by sending and receiving infra-red light through head tissues from scalp sensors (at 690 and 830nm). As a bedside and portable brain-imaging device, NIRS is a promising clinical tool, complementary to functional MRI, fully adapted to study brain activity in realistic lifestyle conditions (physical exercise, walk, gait perturbation, sleep).
The proposed project aims at assessing the ability of EEG and NIRS to monitor attention and workload (e.g. memory, mental calculation task), in fully controlled conditions at rest and then while walking. The first team will aim at characterizing signal patterns that are specific of attention and workload, first during fully controlled experiment. The purpose is to extract those characteristics during controlled experiments, in order to retrieve them in less controlled experiment through machine learning techniques, therefore mimicking situation that would occur when assessing workload during the mission of a soldier or an astronaut. The second important aspect is to ensure optimal data quality when EEG and NIRS data are acquired while walking. The second team will be in charge of developing and evaluating motion correction algorithms, in order to ensure optimal signal quality.
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