Biomagnetic methodologies for the noninvasive investigations of the human brain (Magnobrain)

Magnetoencephalography (MEG) non-invasively infers the distribution of electric currents in the brain by measuring the magnetic fields they induce. Its superb spatial and temporal resolution provides a solid basis for the `functional imaging¿ of the brain provided it is integrated with other brain imaging techniques. MAGNOBRAIN is an applied research project that developed tools to integrate MEG with MRI and EEG. These include: (1) software for MEG oriented MRI feature extraction; (2) the Brain Data Base (BDB) which is a reference library of information on the brain used for more realistic and biologically meaningful functional localisations through MEG and EEG; and (3) a database of normative data (age and sex matched) for the interpretation of MEG. It is expected that these tools will evolve into a medical informatics environment that will aid the planning of neurosurgical operations as well as contribute to the exploration of mental function including the study of perception and cognition

Wireless Brain-Robot Interface: User Perception and Performance Assessment of Spinal Cord Injury Patients

Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Multi-DoF robotics control is possible by patients through commercial wireless BCI. Multiple sessions and tailored BCI algorithms are needed to improve performance