Brain machine interface (BMI) is a technology to directly connect brains and computers. It recently emerges due to the progress in neuroscience, signal processing and machine learning, and has been successfully applied mainly in experimental rooms. In this presentation, I introduce our project, called network-based BMI, which is an application of non-invasive BMI techniques to helping elder and physically handicapped people improve their quality of life. Since they spend much of their time at home, there they have to move around, possibly on wheelchairs, and turn on and off home appliances by themselves. Whether BMI works well to help them control the wheelchair or home appliances in a house without using their body has not been clarified yet. To examine such BMI applicability, we have constructed a real environment like a house, which is called a BMI house. The BMI house is equipped with many sensors, such as ultrasonic sensors, laser range finders, cameras and microphones. Subject’s brain activities are measured in terms of EEG (electroencephalogram) and NIRS (near infrared spectroscopy). EEG-NIRS signals and sensor signals are transmitted to computer servers via wireless network such to keep the data scalability. In addition, such applications must be safe enough; the BMI-controlled wheelchair should never hit upon walls, furniture and other moving objects, so, automatic controls based on position identification of the wheelchair are also involved to ensure the security. This BMI house is also a well-defined real environment; the association of brain activities and sensor signals will provide a large amount of information related to cortical activities in various natural situations in daily living.
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