Detecting Mental States By Machine Learning Techniques: The Berlin Brain-Computer Interface
BRAIN-COMPUTER INTERFACES: REVOLUTIONIZING HUMAN-COMPUTER INTERACTION(2010)
摘要
The Berlin Brain-Computer Interface
(BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for
revealing the user’s mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see
Section 4.1) or mental text entry systems ([1] and see [2–5] for an overview on BCI). In these applications, the BBCI uses
natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user’s intent.
But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other
mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two
examples from our studies are exemplified in Sections 4.3 and 4.4.
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关键词
pattern recognition,machine learning,motor skills
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