Transforming fluorescence spectrometers into costeffective, portable devices provides the potential for field-based applications in biological, environmental, and clinical research and education. However, the majority of developed spectroscopic technologies continue to require heavy, expensive equipment and trained personnel for operation or do not support multispectral analysis, thereby restricting their use in resource-limited environments. Herein, we report a wireless, portable, cost-effective, opensource fluorescence spectrometer (OpenFS) developed by compactly assembling optical and electronic elements in a 3Dprinted housing. OpenFS outputs an accurate emission spectrum over a wide range of wavelengths and demonstrates greater sensitivity for fluorescence quantification compared to a conventional fluorometer. We demonstrate the functionality of OpenFS as a fluorescence resonance energy transfer (FRET)-based DNA sensor by detecting target DNA molecules with FRET efficiency and prove its utility as an Internet of Things device by performing wireless measurements and spectral analysis on a smartphone with a custom-developed Android application. This portable open-source spectrometer can lead to new opportunities in research and educational fields where fluorescence spectroscopy has not been available because of its cost and size and provides the potential for the development of mobile diagnostics platforms.

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In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.