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- W4386159800 abstract "Acquiring large datasets has advantages and disadvantages; an advantage being the bigger the datasets, the more information potentially captured; while a disadvantage being the amount of labor needed to process this information. To combat this disadvantage, researchers in a variety of fields increasingly rely on using tensors to represent high-dimensional data, and then using tensor decompositions to compress the data without losing significant information. One such field is electroencephalography (EEG), which is the study of electrograms that measure brain electrical activity. Having the ability to be continuously recorded for long periods of time, this could be hours, days, even weeks, EEG data can be massive. Here we discuss how tensor decomposition methods such as Parallel Factor (PARAFAC) analysis and Tucker decomposition can be executed on these large datasets." @default.
- W4386159800 created "2023-08-26" @default.
- W4386159800 creator A5052023580 @default.
- W4386159800 creator A5054742849 @default.
- W4386159800 date "2022-12-01" @default.
- W4386159800 modified "2023-09-27" @default.
- W4386159800 title "A Tensor Decomposition in Multi-Way Electroencephalogram (EEG) Data Analysis" @default.
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- W4386159800 doi "https://doi.org/10.1109/csci58124.2022.00017" @default.
- W4386159800 hasPublicationYear "2022" @default.
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