Semantic Mapping for Safe and Comfortable Navigation of a Brain-Controlled Wheelchair

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) …”

“…Nine BCWs assisted with muscle signal publications were found: seven were based on an ERD/ERS signal (36)(37)(38)42,45,51,56), one on P300 signal (40) and one was hybrid of ERD/ERS and P300 signals (52). There was no muscledassisted BCW based on an SSVEP signal.…”

“…Six BCWs used a low-level operation (37,38,45,51,52,56), two high-level (36,42) and one shared control, in which the user could select the type of management by blinking, depending on their needs (40). There was a clear trend to use low-level navigation in muscle-assisted systems, a fact that may be related to the use of an ERD/ERS signal in most of these systems.…”

“…Most ERD/ERS -based BCIs, including those not using muscular tasks, did not use a GUI for their management, since modulation of these signals does not depend on external stimuli. However, in this category of muscle-assisted BCIs, three articles presented a GUI: the proposal of Lin and Yang (38), in which the user switched between the 13 navigation commands by blinking and selected them by performing a state of attention; the BCW of Wei et al (42), in which an environment mapping was shown and the user selected the desired position through different muscle tasks (figure 5); and the interface proposed by Ming et al (51), which showed four stimuli corresponding to the four navigation commands that were illuminated serially, and the participants closed their eyes to select the desired command when it was lit up (figure 6). …”

“…The use of commercial EEG devices has only been tested with muscle-assisted systems, where the EEG is used to detect muscle activity. In two of these interfaces, which used the Emotiv EPOC, only muscle movements were used (37,42), while in the case of the NeuroSky ASIC, the authors used eye-blinking and attention state (38). The use of muscle tasks in commercial systems is given by its lower quality signal acquisition, requiring a stronger signal to be detected more easily.…”