EEG Signal Analysis with Window Slicing

EEG signals may contain many data points, depending on the sampling rate and duration of a recording. Often, it is not feasible to analyze a complete recording due to prohibitive compute and memory requirements which result from an excessive input length. It is, therefore, common to apply window slicing to generate data frames and to incrementally analyze these smaller snippets of a signal rather than a whole recording at once (Tabar and Halici, 2016 (link); Gao et al., 2019 (link)). Thereby, the size of a window and a potential overlap of successive windows are hyper-parameters of the respective analysis and depend on its goal (cp. middle of Figure 2). For example, the detection of slow theta brain waves requires larger windows to capture a full wave within the window while alpha and beta brain waves can be captured in a smaller window.