Wheelchair navigation with an audio-cued, two-class motor imagery-based brain-computer interface system

Abstract

Driving a real wheelchair by means of a braincomputer interface (BCI) system must be a reliable option for locked-in patients. Such navigation should also be autonomous, i.e., not depending on a ground chart. In this work we test the feasibility of driving a customized robotic wheelchair with a BCI system that our group has used in previous studies with virtual and real mobile robots. The results obtained from a sample of three healthy naïve participants suggest that it is an effective option, which could ultimately provide locked-in patients with greater autonomy and quality of life.

Review of real brain-controlled wheelchairs

“…Percentage of hits (37) Qualitative evaluation (38) Time required (40) Success rate, time required and transfer rate (commands per minute) (42) Task success, path length, time, used commands, collisions and obstacle clearance (minimum and average distance to the obstacles) (45) Time optimality rate (51) Success rate and error rate (specified in false positives and false negatives) (52) Task success, path length, time required, path length optimality ratio and time optimality ratio (56) Task success, path length, time required, path length optimality ratio and time optimality ratio, collisions, mean velocity, workload, learnability, confidence and difficulty signal (28,46,57). Other papers used methods such as learning vector quantization in mu and beta bands (43), the logarithmic value in the bands of interest (34,35), the common spatial patterns (CSP) (44,59) …”

“…Just one of the BCWs used a graphical user interface (GUI), the proposal of Zhang et al (59) that was the one with the largest number of commands through the use of a successive dichotomy method. By other side, other article includes the presence of an audio interface, indicating different commands serially that users could select through righthand MI (57). Hence, the only two proposal based purely on ERD/ERS signal whose CTR outperformed the unit were those that needed a specific graphical or auditory serial interface.…”

Switch Mode to Control a Wheelchair Through EEG Signals

“…The subject initially participated in one initial training session for calibration purposes in which, immersed in a virtual environment (VE), he had to control the displacement of a car to the right through right hand motor imagery (MI) task, or to maintain the car in a straight line through a relaxed state [5], [6]. Signal processing consisted of extracting EEG parameters and classifying them according to the two mental tasks.…”

“…The navigation paradigm was identical to the one used in [5], [6]. In order to control the wheelchair, a graphical interface was presented to the users in order to offer them several navigation commands, which were selected through…”

“…This paradigm has been used successfully to control a wheelchair using discrete movements [5] however, other strategies should be considered as for example, providing continuous movements [6]. In the present study, a new strategy based on switch mode control which have already been tested in a virtual wheelchair [7] is presented.…”