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Evolving Signal Processing for Brain-Computer Interfaces
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DOI：10.1109/JPROC.2012.2185009.png)
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Abstract
En 中文
Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human-machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in the future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them.
Keywords:
Blind source separation (BSS)
brain-computer interface (BCI)
cognitive state assessment
effective connectivity
electroencephalogram (EEG)
independent component analysis (ICA)
machine learning (ML)
multimodal signal processing
signal processing
source-space modeling
transfer learning
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