36 research outputs found
Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network
Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals
Selective deposition of dietary α-Lipoic acid in mitochondrial fraction and its synergistic effect with α-Tocoperhol acetate on broiler meat oxidative stability
Capacidade dos biomarcadores inflamatĂłrios em predizer a sĂndrome metabĂłlica: Inflammation biomarkers capacity in predicting the metabolic syndrome
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Molecular sensing using hyperpolarized xenon NMR spectroscopy
Molecular imaging is the determination of the spatial location and concentration of specific molecules in a sample of interest. Sophisticated modern magnetic resonance imaging machines can collect NMR spectra from small-volume elements within a sample, enabling local chemical analysis. However, abundant water and fat signals limit detection of metabolites to near mm concentrations. Alternatively, targeted relaxation contrast agents enhance the relaxation of the strong water signal where they bind. A comparison of images with and without a contrast agent shows the target distribution, but high mm concentrations are needed. We have developed an approach that exploits the strong signals of hyperpolarized 129Xe (an inert reporter introduced for imaging). The imaging contrast agents are composed of a biological recognition motif to localize the agent (antibodies or aptamers) and covalently tethered cryptophane cages. Xenon binds to the cryptophane and though chemical exchange saturation transfer creates contrast in a xenon image. Imaging agents can deliver many cages per target, giving detection limits in the pm concentration range. The evolution and principles of this approach are described herein. © 2014 Wiley-VCH Verlag GmbH&Co. KGaA, Weinheim