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The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS) unified framework to summarize the correlation analysis (CA)-based SSVEP spatial filtering methods from a machine learning perspective. Within this framework, the commonalities and differences between various spatial filtering methods appear apparent, the interpretation of computational factors becomes intuitive, and spatial filters can be determined by solving a generalized optimization problem with non-linear and regularization items. Moreover, the proposed LS framework provides the foundation of utilizing the knowledge behind these spatial filtering methods in further classification/regression model designs. Through a comparative analysis of existing representative spatial filtering methods, recommendations are made for the superior and robust design strategies. These recommended strategies are further integrated to fill the research gaps and demonstrate the ability of the proposed LS framework to promote algorithmic improvements, resulting in five new spatial filtering methods. This study could offer significant insights in understanding the relationships between various design strategies in the spatial filtering methods from the machine learning perspective, and would also contribute to the development of the SSVEP recognition methods with high performance.

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Background: This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019 (COVID-19) case numbers in London from January 31, 2020 to February 24, 2022. The primary objective was to enhance understanding of the interactions among government pandemic responses, viral mutations, and the subsequent changes in COVID-19 case incidences. Methods: We employed the adaptive Fourier decomposition (AFD) method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three elements. These restructured components were rigorously evaluated using Pearson correlation, and their effectiveness was compared with other signal analysis techniques. This study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition methods. Results: Analysis of London's daily COVID-19 data using AFD revealed a strong correlation between the "stay at home" directive and high-frequency components during the first epidemic wave. This indicates the need for sustained implementation of vaccination policies to maintain their effectiveness. Discussion: The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This robust tool has proven invaluable for analyzing COVID-19 pandemic data, offering critical insights that guide the formulation of future preventive and public health strategies.