Abstract
SPECTER (the Signal sPECtrum Tensor decomposition and Eye blink Removal) is a
novel algorithm designed to detect and elimininate eye blink-related artifacts from electroen-
cephalogram (EEG) recordings. Our previous study [1] demonstrated its superior performance
compared to established regression-based methods or independent component analysis, espe-
cially in situations where these approaches failed to accurately detect eye blinks or introduced
spurious oscillations into the signal. In this study, we introduce SPECTER 2.0, an improved ver-
sion that addresses the limitations of the original algorithm, and we demonstrate its improved
performance on a real EEG dataset affected by eye blinks.
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