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- W2396154410 abstract "This paper exhibits two methods for decreasing the time associated with training a machine learning classifieron biometric signals. Using electroencephalography (EEG) data obtained from a consumer-grade headset witha single electrode, we show that these methods produce significant gains in the computational performanceand calibration time of a simple brain-computer interface (BCI) without significantly decreasing accuracy. Wediscuss the relevance of reduced feature vector size to the design of physiological computing applications." @default.
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- W2396154410 date "2015-01-01" @default.
- W2396154410 modified "2023-09-27" @default.
- W2396154410 title "Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique" @default.
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- W2396154410 doi "https://doi.org/10.5220/0005238800440051" @default.
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