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Lateral Flow Assays (LFA) have been one of the most widely adopted technologies in clinical diagnosis over recent years, especially during the COVID-19 pandemic, due to their feasibility, compactness, and rapid readout. However, the precise dispensing of antibodies–a key part of the fabrication process–requires costly line dispenser equipment, which poses a challenge to researchers with limited budgets. This study aims to resolve this key issue by introducing a Syringe-based Arduino-operated Low-cost Antibody Dispenser (SALAD). By utilizing a microneedle, stepper motor-driven syringe pump, and conveyor belt, SALAD can form micro-droplets to create an even band of antibodies. Our evaluation results showed comparable performance between SALAD and a commercialized model – Claremont ALFRD, with SALAD exceeding in affordability and feasibility. SALAD yielded an even signal, uniform bandwidth, and low background noise, yet optimization in the conveyor belt should be considered to enhance stability. With a low manufacturing cost ($200.61) compared to the commercialized models, our model is expected to provide an affordable approach for LFA researchers.

Enhancing the performance of motor imagery-based Brain-Computer Interfaces (BCI) has been a significant goal in the BCI field. To achieve such a goal, several typical and promising techniques have been implemented, such as developing intelligent algorithms, combining features from different domains, extracting subject-specific parameters, and so forth. Previous studies performing temporal segmentation often ended up with a large number of features and placed a burden on computational cost, which poses a disadvantage to online analysis. This study proposes a novel approach to utilizing short-window segments to find an optimal combination of time segments and feature extractors. Electroencephalogram data from four datasets (BCI Competition IV dataset 2a, 2b and two self-acquired datasets) were segmented into four types of the time window and had features extracted by Common Spatial Pattern and its variants, and lastly classified by Linear Discriminant Analysis. The result shows that the combination of the “2-s with 1-s overlapping” segment and Filter Bank Common Spatial Pattern yields overall accuracy of 2–6.5% (p-value