A case-study of using tensors in multi-way electroencephalograme (EEG) data analysis

Tensors are multi-dimensional arrays that can represent large datasets. Acquiring large data sets has its pros and cons; a pro being the bigger the data set, the more information could potentially be generated. A con would be the amount of labor needed to process this information. To combat this con, researchers in a variety of fields rely on tensor decomposition. Tensor decomposition's goal is to compress the data without losing any signficant information. Tensor decompositon is also known for its ability to extract underlying features that could not have been seen at face value. One such field is electroencephalography, which is the study of electrograms (EEG). An electrogram is a brain imaging tool that measures brain electrical activity. Having the abilitiy to be continously recorded for long periods of time, this could be hours, days, even weeks, EEG tends to have massive multi-dimensional datasets. In order to process the data, tensor decompositon methods such as Parallel Factor Analysis (PARAFAC) and Tucker decomposition can be executed on these large datasets.