Research areas

Virtual Reality (VR) offers a unique research tool in behavioural neuroscience to investigate how humans interact with their surroundings under realistic conditions, allowing both precise control over stimuli (i.e the virtual world) and experience of a realistic interactive environment. VR has been successfully used in studies on obstacle avoidance, interceptive actions, path perception, and visuo-locomotor adaptation. Recently, research studies have focused on the development of novel robotic interfaces working in conjunction with Virtual Reality (VR) systems for a more efficient neuro-motor rehabilitation of stroke patients.

We study human interaction with the environment in different conditions and in non-clinical and clinical populations. The novelty of our approach resides in the combination of behavioral observation and functional monitoring, respectively performed with VR, tracking devices (e.g. Trackhold) and HR-EEG systems. Particularly interesting is the possibility of using the results of human movement analysis to map behavioral information onto the brain activity, in order to correlate motor tasks planning/execution to brain commands and environmental interaction.

We study the behavioral and functional mechanisms underpinning motor coordination, with particular emphasis to the interaction between motor behavior and brain activity. We aim at contributing to a better understanding of such interaction during the life span of humans, from the development of interconnected motor and brain functions during the perinatal age, to the regression of those functions with aging. Our studies are performed by combining behavioral observations and kinematics acquisitions with the monitoring of the brain function by means of fMRI, EEG and MEG systems.

We investigate the psychological (cognitive, emotional, motivational), biological (bodily and somatovisceral), social (communicative), biomechanical, and behavioral factors associated with the development, improvement, and optimization of motor performance in the context of physical activity and sport. We adopt the theoretical frame of the Individual Zones of Optimal Functioning (IZOF) model that provides a comprehensive conceptualization of psycho-bio-social (PBS) states related to performance. The IZOF model defines PBS states as situational, multimodal, and dynamic manifestations of total human functioning. This conceptualization is consistent with current holistic views that integrate the structural components of human performance, such as emotional/cognitive/motor processes, and the neurophysiological basis of these structural components. The specificity of our approach resides in the integration of behavioral, biological, and psychological data with functional brain monitoring, respectively performed with biomechanical, biomedical, and psychophysical methods including HR-EEG recording. Data acquisition and synchronization are performed by means of Powerlab Acquisition System. By using the results of structural components of human performance we aim at mapping behavioral and emotional information onto the brain activity. This is an interesting approach that permits to correlate motor tasks planning/execution and the emotional influence with brain commands.

The physiological data collected with biomagnetic (MCG and infant MEG), EEG and fMRI systems, are analyzed using both linear and nonlinear signal processing techniques. Depending on the information of interest to be extracted from the recorded signals, diverse type of signal processing techniques can be performed.

Since 2004, our group has extensively employed Independent Component Analysis (ICA) for the processing of biomedical data of different origin, with the purpose of separating physiological signals and denoising the signals of interest. Signal denoising is also achieved by means of techniques that utilize wavelets decomposition.

More recently, our interest has focused on the development of methods to assess the non linear dynamics of complex physiological systems such as the heart or the brain, and to perform an automatic pattern recognition between clinical and non clinical data sets.


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August 2017
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