A Human-Humanoid Interaction Through the Use of BCI for Locked-In ALS Patients Using Neuro-Biological Feedback Fusion

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2018-02, Vol.26 (2), p.487-497
• Main Authors: Sorbello, Rosario, Tramonte, Salvatore, Giardina, Marcello Emanuele, La Bella, Vincenzo, Spataro, Rossella, Allison, Brendan, Guger, Christoph, Chella, Antonio
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; ALS patients; Amyotrophic lateral sclerosis; Amyotrophic Lateral Sclerosis - rehabilitation; Attention; Attention task; BCI; Biofeedback; Biofeedback, Psychology - methods; Brain; Brain-Computer Interfaces; Computer applications; Computer architecture; EEG; Electroencephalography; Entropy; Event-Related Potentials, P300; Eye Movements; Feature extraction; Feedback; Female; Healthy Volunteers; Human-computer interface; human-humanoid robot interaction; Humanoid; Humanoid robots; Humans; Implants; locked-in patients; Male; Motivation; neuro-biological feedback fusion; Patients; Prosthesis Design; Psychomotor Performance; Quadriplegia - rehabilitation; Robotics; Stress
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2017.2728140

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 B 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.