A system and method for a biological interface system that processes multicellular signals of a patient and controls one or more devices is disclosed. The system includes a sensor that detects the multicellular signals and a processing unit for producing the control signal based on the multicellular signals. The system further includes an automated configuration routine that is used to set or modify the value of one or more system configuration parameters.

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Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the E´ cole Polytechnique Fe´de´ rale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

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Increasing survival rates in severe illnesses and traumatic injuries have led to an increase in the number of disabled people of working age or younger. According to the World Health Organization (WHO), there are about 650 million people with disabilities world-wide. Education is one of the factors that can help them overcome their own limitations. According to the Polish Government Plenipo-tentiary for Disabled People, in 2009 only 5.9% of disabled Poles had a degree, and only 21.4% were employed, which reflects the mar-ginalization of the disabled from the rest of Polish society. Distance learning may offer hope for breakthroughs in the area of edu-cation for people with disabilities. This article aims at investigating the extent to which the available opportunities are being ex-ploited, including e-learning as a part of the concept of the disabled person’s integrated IT environment.

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The EEG Processing Unit comprises a semi-rigid framework which substantially conforms to the Patient’s head and supports a set of electrodes in predetermined loci on the Patient’s head to ensure proper electrode placement. The EEG Processing Unit includes automated connectivity determination apparatus which can use pressure-sensitive electrode placement ensuring proper contact with Patient’s scalp and also automatically verifies electrode placement via measurements of electrode impedance through automated impedance checking. Voltages generated by the electrodes are amplified and filtered before being transmitted to an analysis platform, which can be a Physician’s laptop computer system, either wirelessly or via a set of tethering wires. The EEG Processing Unit includes an automatic artifacting capability which identifies when there is sufficient clean data compiled in the testing session. This process automatically eliminates muscle- or other physical-artifact-related voltages. Clean data, which represents real brain voltages as opposed to muscle- or physical-artifact-related voltages, thereby are produced.

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In this study, a next-generation Kansei-equipment agricultural robot is proposed and focuses jn controlling the robot through body language. Kansei agri-robots are defined as agricultural robots equiped with Kansei communication and robotic Functions. Kansei communication is defined as «two-way communication between humans and robots, or other machines, that supplements or supersedes traditional one-way (human-to-machine) input and operation».

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