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- W2411486284 abstract "The use of brain signals for interaction and for controlling robots and prosthetic devices is a rapidly emerging field of multidisciplinary research called brain–machine interfaces (BMI), or brain–computer interfaces (BCI), which has seen impressive achievements in recent years. A BMI monitors the user’s brain activity, extracts specific features from the brain signals that reflect the intent of the subject, and translates their intentions into actions – such as closing the prosthetic hand – without the activity of any muscle or peripheral nerve. Adaptation is a key component of a BMI, because, on the one hand, users must learn to modulate their brainwaves so as to generate distinct brain patterns, while, on the other hand, machine learning techniques need to discover the individual brain patterns characterizing the mental tasks executed by the user. This chapter provides an introduction to the field of BMI, with a particular focus on principles for reliable and long-term operation of neuroprostheses. BMI offers a natural way to aid patients with severe neuromuscular disabilities, and also opens up new possibilities in human–machine interaction for able-bodied people. This chapter reviews the different kinds of brain signals that can be used as input for a BMI and discusses a series of principles to build efficient BMIs that are independent of the particular signal of choice. These principles concern the nature of electrical brain correlates more suitable to control neuroprosthetic devices and to promote motor rehabilitation, the use of machine learning techniques, and the design of context-aware BMIs. The authors conclude by discussing some future research directions in the field of BMI." @default.
- W2411486284 created "2016-06-24" @default.
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- W2411486284 date "2003-05-01" @default.
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- W2411486284 title "Research news and notes" @default.
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- W2411486284 doi "https://doi.org/10.1016/s0090-3019(03)00175-7" @default.
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