The spectral characteristics viz. reflectivity, bandwidth, and sidelobes’ intensity for uniform and apodized (Gaussian, hyperbolic tangent, apod1, sine, and raised sine) fiber Bragg gratings (FBGs) were studied as a function of grating length and index modulation. The optimal grating length and index modulation to obtain maximum reflectivity and minimum sidelobes were determined, as needed for sensing applications. The impact of various apodization profiles on the spectral response has also been assessed. The results indicate that out of the apodization profiles considered for the study, sine, Gaussian, and raised sine profiles offer the desired output. Article highlights The reflectivity (of main peak) and sidelobes’ intensity increase with grating length and index modulation. The bandwidth decreases with grating length and increases with index modulation. The ideal grating length and index modulation were found to be 5 mm and 0.0008 respectively to obtain maximum reflectivity and minimum intensity for sidelobes. Sine, Gaussian, and raised sine profiles are the best suitable apodization profiles among those considered.

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Currently, the operational electroencephalography (EEG)-based brain–computer interfaces (BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces impractical scenarios. One of the reasons behind the present challenges is the application of a purely data-driven approach to the BCI pipeline. Although BCI applications have improved significantly with the research in the fields of artificial intelligence (AI) and machine learning (ML), fundamental issues of data-driven training restrict the latency that can be achieved under current BCI paradigms. This work explores the possibility of future BCI using a combination of data-driven and theory-driven methods. In this study, an EEG-BCI dataset from steady-state visually evoked potentials (SSVEPs) is applied, where the SSVEP signals contain, source components from the occipital, parietal and frontal regions of the brain. Source reconstruction is done with the combination of independent component analysis (ICA) and low-resolution electromagnetic tomography analysis (LORETA). This method was able to predict BCI classification labels 5[Formula: see text]s earlier, based on pre-recorded signals from the scalp. The novelty of the current contribution lies in utilizing the source reconstructed EEG time-series for BCI classification, which allows for retention of classification accuracy up to 70% while working with the reduced data dimensionality. Implementation of this algorithm will allow a significant reduction in lag in online BCIs.