A BCI using VEP for continuous control of a mobile robot

Kapeller, Christoph; Hintermüller, Christoph; Abu-Alqumsan, Mohammad; Prückl, Robert; Peer, Angelika; Guger, Christoph

doi:10.1109/embc.2013.6610734

Cited by 31 publications

(30 citation statements)

References 18 publications

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“…Another study with a c-VEP BCI including 11 subjects reported a grand average classification accuracy of 98.18% with nobody below 90% accuracy (Kapeller et al, 2013). This study showed that a BCI using c-VEPs works even for subjects who could not use an SSVEP BCI.…”

Section: Discussionmentioning

confidence: 99%

An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study

Kapeller

Kamada

Prueckl

et al. 2014

Front. Syst. Neurosci.

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A brain-computer-interface (BCI) allows the user to control a device or software with brain activity. Many BCIs rely on visual stimuli with constant stimulation cycles that elicit steady-state visual evoked potentials (SSVEP) in the electroencephalogram (EEG). This EEG response can be generated with a LED or a computer screen flashing at a constant frequency, and similar EEG activity can be elicited with pseudo-random stimulation sequences on a screen (code-based BCI). Using electrocorticography (ECoG) instead of EEG promises higher spatial and temporal resolution and leads to more dominant evoked potentials due to visual stimulation. This work is focused on BCIs based on visual evoked potentials (VEP) and its capability as a continuous control interface for augmentation of video applications. One 35 year old female subject with implanted subdural grids participated in the study. The task was to select one out of four visual targets, while each was flickering with a code sequence. After a calibration run including 200 code sequences, a linear classifier was used during an evaluation run to identify the selected visual target based on the generated code-based VEPs over 20 trials. Multiple ECoG buffer lengths were tested and the subject reached a mean online classification accuracy of 99.21% for a window length of 3.15 s. Finally, the subject performed an unsupervised free run in combination with visual feedback of the current selection. Additionally, an algorithm was implemented that allowed to suppress false positive selections and this allowed the subject to start and stop the BCI at any time. The code-based BCI system attained very high online accuracy, which makes this approach very promising for control applications where a continuous control signal is needed.

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Section: Discussionmentioning

confidence: 99%

An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study

Kapeller

Kamada

Prueckl

et al. 2014

Front. Syst. Neurosci.

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“…The BCI user interface is based upon the Screen Overlay Control Interface (SOCI) library [4], which allows embedding the BCI stimuli on top of the native interface of any user application and communicates with the BCI hardware via a network connection.…”

Section: User Stationmentioning

confidence: 99%

Brain Neural Computer Interface for Everyday Home Usage

Hintermüller

Vargiu

Halder

et al. 2015

Universal Access in Human-Computer Interaction. Access to Interaction

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Abstract. In the last years, Brain Neural Computer Interfaces (BNCIs) have been investigated and several applications have been proposed. Those systems have been explored almost exclusively in laboratories with developers and researchers. Home usage has been demonstrated, though only with on-going expert supervision. In this paper, we present a BNCI for everyday home usage. The proposed system is aimed at supporting the autonomy and independence of people living at home with a disability. The overall system is currently installed in three end-users' home in Belfast.

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“…Among BCIs, c-VEP has been shown to be superior with respect to the accuracy, Information Transfer Rate (ITR) and the possibility of using many targets in applications [4], [6]. In a recent study, it has been specifically indicated that the c-VEP based BCI is the most suitable scheme for controlling a robotic device [7]. c-VEPs are repetitive potentials elicited in the occipital lobe of the brain, when the person is focusing on a visual stimulus, flickering in a repetitive pattern.…”

Section: Introductionmentioning

confidence: 99%

A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential

Mohebbi

Engelsholm

Puthusserypady

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this pilot study, a novel and minimalistic Brain Computer Interface (BCI) based wheelchair control application was developed. The system was based on pseudorandom code modulated Visual Evoked Potentials (c-VEPs). The visual stimuli in the scheme were generated based on the Gold code, and the VEPs were recognized and classified using subject-specific algorithms. The system provided the ability of controlling a wheelchair model (LEGO(®) MINDSTORM(®) EV3 robot) in 4 different directions based on the elicited c-VEPs. Ten healthy subjects were evaluated in testing the system where an average accuracy of 97% was achieved. The promising results illustrate the potential of this approach when considering a real wheelchair application.

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A BCI using VEP for continuous control of a mobile robot

Cited by 31 publications

References 18 publications

An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study

An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study

Brain Neural Computer Interface for Everyday Home Usage

A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential

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