In this work, we introduce the AMUSET-TICA method for generating CVs for biomolecular dynamics. The methodological advancements of AMUSET-TICA are threefold: the use of tICs as input features instead of geometric features (e.g., pairwise distances or torsional angles), the expansion of tICs using overlapping Gaussian basis functions, and the stacking of these Gaussian functions by applying all basis functions to every tIC. Together, these Gaussian basis functions in AMUSET-TICA enable an effective embedding of the phase space. Moreover, AMUSET-TICA addresses a key challenge of AMUSEt by eliminating the need for manual selection and ranking of input features based on physical intuition. We demonstrate the efficacy of AMUSET-TICA using alanine dipeptide, NTL9, and the FIP35 WW domain. For all systems, AMUSET-TICA outperforms both AMUSEt and tICA in accurately predicting the slowest CVs. In addition, we demonstrate that AMUSET-TICA is approximately four times less computationally expensive in terms of CPU time than the deep-learning-based VAMPnets while achieving comparable accuracy for both NTL9 and the WW domain. We anticipate that AMUSET-TICA will be widely applicable for efficiently identifying CVs in biomolecular dynamics.