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Art and Honda Successfully Develop New Brain-Machine Interface

Honda’s cutting edge research and development activities go well beyond the world of transportation, evident by a recent collaborative effort between the Advanced Telecommunications Research Institute International (ATR) and Honda Research Institute Japan Co., Ltd. (HRI).

The two Institutes recently announced successful development of a new “Brain Machine Interface” (BMI) for manipulating robots using brain activity signals.

The new BMI technology has enabled the decoding of natural brain activity and the use of the extracted data for the near realtime operation of a robot without an invasive incision of the head and brain.

This breakthrough facilitates greater possibilities for new types of interface between machines and the human brain.

The idea of this BMI technology is based on a highly acclaimed article titled “Decoding the perceptual and subjective contents of the human brain” by Dr. Yukiyasu Kamitani, a researcher at ATR Computational Neuroscience Laboratories, which recently appeared in a leading science journal, Nature Neuroscience.

For this study, Dr. Kamitani was named by Scientific American magazine as Research Leader, with his collaborator Dr. Frank Tong at Vanderbilt University, within the 2005 Scientific American 50 – the magazine’s prestigious annual list that recognizes outstanding acts of leadership in science and technology. HRI and ATR have developed the article’s theory into a system for real-time brain activity decoding and robotic control.

This research reveals that MRI-based neural decoding can allow a robot hand to mimic the subject’s finger movements (“paper-rock-scissors”) by tracking the hemodynamic responses in the brain. Although there is an approximate seven second time lag between the subject’s movement and the robot’s mimicking movement, the researchers succeeded in gaining a decoding accuracy of 85%.

This technology is potentially applicable to other types of non-invasive brain measurements such as the brain’s electric and magnetic fields and brain waves. By utilizing such methods, it is expected that the same result could be achieved with less time lag and more compact BMI system devices.

For the full article, please visit New Brain-Machine Interface.