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In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those utilizing steady-state visual-evoked potentials (SSVEPs). However, the conventional short-time Fourier transform (STFT) analysis is associated with the low-frequency resolution because of the length of the analysis window, resulting in sidelobe artifacts. In this paper, we utilized the non-harmonic analysis (NHA), which does not depend on the length of the analysis window, to analyze the continuous changes in and determine the classification accuracy of SSVEPs. Moreover, our experiments utilized the gray-scale images, allowing for the presentation of the stimulus as a sinusoidal pattern and reducing the effect of frequency distortion associated with the refresh rate of the liquid-crystal display. Our findings indicated that NHA resulted in exponential improvements in time-frequency resolution when compared with the STFT analysis. As the accuracy of NHA was high, our results suggest that this method is effective for examining SSVEPs and changes in brain waves during experiments conducted using liquid-crystal displays. INDEX TERMS Brain computer interface, chirp stimulus, grayscale images, non-harmonic analysis, steady-state visual evoked potential.

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