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RESEARCH PRODUCT
A Human-Humanoid Interaction Through the Use of BCI for Locked-In ALS Patients Using Neuro-Biological Feedback Fusion.
Rosario SorbelloSalvatore TramonteMarcello Emanuele GiardinaVincenzo La BellaRossella SpataroBrendan AllisonChristoph GugerChristoph GugerAntonio ChellaAntonio Chellasubject
MaleEye MovementsBCI Locked-In Patients ALS Patients Human-Humanoid Robot Interaction neuro-biological feedback fusionmedicine.medical_treatment02 engineering and technology0302 clinical medicineAttentionBCIAmyotrophic lateral sclerosiseducation.field_of_studyGeneral NeuroscienceRehabilitationlocked-in patientsRoboticsElectroencephalographyRoboticsHealthy VolunteersBrain-Computer InterfacesFemalePsychologyHumanoid robotAlgorithmsAdultmedicine.medical_specialty0206 medical engineeringPopulationhuman-humanoid robot interactionBiomedical EngineeringBiofeedbackProsthesis DesignQuadriplegia03 medical and health sciencesPhysical medicine and rehabilitationEvent-related potentialInternal MedicinemedicineHumanseducationBrain–computer interfacebusiness.industryAmyotrophic Lateral SclerosisEye movementBiofeedback Psychologymedicine.disease020601 biomedical engineeringEvent-Related Potentials P300neuro-biological feedback fusionALS patientsArtificial intelligencebusiness030217 neurology & neurosurgeryPsychomotor Performancedescription
This paper illustrates a new architecture for a human–humanoid interaction based on EEG-brain computer interface (EEG-BCI) for patients affected by locked-in syndrome caused by Amyotrophic Lateral Sclerosis (ALS). The proposed architecture is able to recognise users’ mental state accordingly to the biofeedback factor $\text {B}_{\text f}$ , based on users’ attention, intention, and focus, that is used to elicit a robot to perform customised behaviours. Experiments have been conducted with a population of eight subjects: four ALS patients in a near locked-in status with normal ocular movement and four healthy control subjects enrolled for age, education, and computer expertise. The results showed as three ALS patients have completed the task with 96.67% success; the healthy controls with 100% success; the fourth ALS has been excluded from the results for his low general attention during the task; the analysis of ${B}_{f}$ factor highlights as ALS subjects have shown stronger ${B}_{f}$ (81.20%) than healthy controls (76.77%). Finally, a post-hoc analysis is provided to show how robotic feedback helps in maintaining focus on expected task. These preliminary data suggest that ALS patients could successfully control a humanoid robot through a BCI architecture, potentially enabling them to conduct some everyday tasks and extend their presence in the environment.
year | journal | country | edition | language |
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2018-01-01 | IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society |