A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots

• Published in: Clinical neurophysiology Vol. 119; no. 9; pp. 2159 - 2169
• Main Authors: Galán, F., Nuttin, M., Lew, E., Ferrez, P.W., Vanacker, G., Philips, J., Millán, J. del R.
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 01.09.2008; Elsevier Science
• Subjects: Asynchronous protocol; Biological and medical sciences; Brain - physiology; Brain Mapping; Brain–computer interfaces; Electrodiagnosis. Electric activity recording; Electroencephalogram (EEG); Electroencephalography - methods; Feature selection; Fundamental and applied biological sciences. Psychology; General aspects. Models. Methods; Humans; Intelligent wheelchair; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Nervous system; Neurology; Robotics; Shared control; User-Computer Interface; Vertebrates: nervous system and sense organs; Wheelchairs; Feature selection; Asynchronous protocol; Electroencephalogram (EEG); Intelligent wheelchair; Brain–computer interfaces; Shared control; Human; User; Selection; Central nervous system; Electrophysiology; Electroencephalography; Sound; Brain-computer interfaces; Algorithm; Encephalon; Brain-computer interface; Electrodiagnosis; Feasibility; Robustness; Trajectory; Performance; Intelligent system
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/j.clinph.2008.06.001

To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain–Computer Interface (BCI) for continuous mental control of a wheelchair. In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1 h to 2 months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI.