Robot Control Using an Inexpensive P300 Based BCI

Bahman, Shafie; Shamsollahi, Mohammad Bagher

doi:10.1109/icbme49163.2019.9030408

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…This system blinks the characters displayed on the display sequence and determines the character the user is paying attention to from the P300 appearance timing. In addition, as an advanced form of the P300 Speller, research is underway to present life-related conceptual diagrams instead of letters and control the robot environment according to the results selected by the user [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Image Presentation Method for Human Machine Interface Using Deep Learning Object Recognition and P300 Brain Wave

Nakajima

Rusydi

Ramadhani

et al. 2022

JOIV : Int. J. Inform. Visualization

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Welfare robots, as a category of robotics, seeks to improve the quality of life of the elderly and patients by availing a control mechanism to enable the participants to be self-dependent. This is achieved by using man-machine interfaces that manipulate certain external processes like feeding or communicating. This research aims to realize a man-machine interface using brainwave combined with object recognition applicable to patients with locked-in syndrome. The system utilizes a camera with pretrained object-detection system that recognizes the environment and displays the contents in an interface to solicit a choice using P300 signals. Being a camera-based system, field of view and luminance level were identified as possible influences. We designed six experiments by adapting the arrangement of stimuli (triangular or horizontal) and brightness/colour levels. The results showed that the horizontal arrangement had better accuracy than the triangular method. Further, colour was identified as a key parameter for the successful discrimination of target stimuli. From the paper, the precision of discrimination can be improved by adopting a harmonized arrangement and selecting the appropriate saturation/brightness of the interface.

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Section: Introductionmentioning

confidence: 99%