Neuroscience Meets Data Science: Exploring Common Spatial Pattern (CSP) and Its Applications in Healthcare Analytics

Common Spatial Pattern (CSP) is a popular technique in the field of biomedical signal processing and has been widely used in various applications, particularly in the healthcare industry. It is a spatial filtering technique that is used to extract features from multi-channel biomedical signals such as electroencephalogram (EEG) or magnetoencephalogram (MEG). The goal of CSP is to find a set of spatial filters that can effectively differentiate between two classes of signals based on their covariance matrices. The mathematical foundation of CSP is based on linear algebra and multivariate statistical methods. CSP involves transforming the EEG data from the time domain to the spatial domain using a spatial filter. It applies a spatial filter to the multi-channel EEG data to enhance the signal variance of one class while reducing it for the other within the same domain. This process results in new features (components) that are linear combinations of the original channels. The goal of the spatial filter is to find a set of spatial weights that maximally discriminate between two or more classes of EEG data. <a href="https://medium.com/geekculture/common-spatial-pattern-and-its-applications-in-the-healthcare-industry-faa4311dab79">Click Here</a>