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

Rio Nakajima - Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
Muhammad Ilhamdi Rusydi - Universitas Andalas, Padang City, 25164, Indonesia
Salisa Asyarina Ramadhani - Universitas Andalas, Padang City, 25164, Indonesia
Joseph Muguro - Dedan Kimathi University of Technology, Private Bag, Nyeri 10143, Kenya
Kojiro Matsushita - Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
Minoru Sasaki - Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan

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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.


Image; human machine interface; electroencephalogram; object recognition; P300.

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