Electric wheelchair control system using brain-computer interface based on alpha-wave blocking

Review of real brain-controlled wheelchairs

“…Although in most proposals of this section the ERD/ERS signal was used besides the muscular movement, the feature extraction methods were diverse: PCA together with CSP to detect MI tasks and actual face movements (36), only CSP to detect the same states (45), the signal averaging to detect blinks or an open/close eyes state (51,56), the integral of energy in different bands to detect different facial movements (42) or a hidden Markov model to detect the gaze direction (56). Furthermore, the characteristics of the signal P300 was extracted through CSP in the proposal of Wang et al (52) while in the BCW of Puanhvuan and Wongsawat (40) a magnitude summing method was presented.…”

“…Other common method was used, the CCA, however not for the analysis of SSVEP signal but for the eye-blinking detection with a threshold method. There are other proposals with a threshold method (51) or maximum detection found in the proposal of Puanhvuan and Wongsawat (40). The BCW of Choi (36) used EMG in order to generate a stop command through a threshold method where a value was first decided, and if the EMG value exceeds this threshold value, the command execution was stopped.…”

“…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.…”