Machine-Learning Methods for Decoding Intentional Brain States
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Max Planck Institute for Biological Cybernetics, Germany
Brain-computer interfaces (BCI) work by making the user perform a specific mental task, such as imagining moving body parts or performing some other covert mental activity, or attending to a particular stimulus out of an array of options, in order to encode their intention into a measurable brain signal. Signal-processing and machine-learning techniques are then used to decode the measured signal to identify the encoded mental state and hence extract the user's initial intention. The high-noise high-dimensional nature of brain-signals make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn't matter what classifier you use once your features are extracted." Using examples from our own MEG and EEG experiments, I'll demonstrate how machine-learning principles can be applied in order to improve BCI performance, if they are formulated in a domain-specific way. The result is a type of data-driven analysis that is more than "just" classification, and can be used to find better feature extractors.
Conference:
Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010.
Presentation Type:
Oral Presentation
Topic:
Brain-computer and neural interfacing
Citation:
Hill
J
(2010). Machine-Learning Methods for Decoding Intentional Brain States.
Front. Neurosci.
Conference Abstract:
Biomag 2010 - 17th International Conference on Biomagnetism .
doi: 10.3389/conf.fnins.2010.06.00252
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Received:
01 Apr 2010;
Published Online:
01 Apr 2010.
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Correspondence:
Jeremy Hill, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, jez@tuebingen.mpg.de