A self-paced BCI system to control an electric wheelchair: Evaluation of a commercial, low-cost EEG device

Carrino, Francesco; Dumoulin, Joël; Mugellini, Elena; Khaled, Omar Abou; Ingold, Rolf

doi:10.1109/brc.2012.6222185

Cited by 50 publications

(39 citation statements)

References 11 publications

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“…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.…”

Section: Muscle-assisted Bcwsmentioning

confidence: 99%

“…However, there was one proposal which used the raw signal, the BCW of (38). On the other hand, about the classification methods, the most used are, again, SVM (36,52,56) and LDA (37,45). 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.…”

Section: Feature Extraction and Classification Methodsmentioning

confidence: 99%

“…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) …”

Section: Muscle-assisted (36)mentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

“…Most systems used stimuli in the mentioned range of 5 to 20 Hz, with the exception of the study of Diez et al (50), in which they proved that it is possible to achieve an adequate control of a BCW using high frequencies (37)(38)(39)(40). The proposal of this study comes from the knowledge that highfrequency flickering stimuli produce less eye strain than low and medium frequencies (68), which is one of the main drawbacks of an SSVEP BCI.…”

mentioning

confidence: 99%

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Review of real brain-controlled wheelchairs

2016

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Abstract. This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface (BCI). Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic (EEG) signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future.

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