Liquid-crystal display as a spectral selector for visible spectrometers

The polarization properties of a twisted nematic liquid crystal display (TNLCD) are studied experimentally with the aim of using it as a wavelength selector. The output of a white LED is split into its constituent wavelengths with a resolution of 2-5 nm in proportion to a voltage applied to the TNLCD. The feasibility of employing the display as a wavelength selector in visible spectrometers is demonstrated. A simple inexpensive design of a spectrometer built around an LED and a TNLCD is suggested.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.