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We propose a novel filter-type equalizer to improve the solution of the linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational complexity constrained to be quadratic in the filter length. When highorder modulations and/or large memory channels are used, the optimal BCJR equalizer is unavailable, due to its computational complexity. In this scenario, the filter-type LMMSE turbo equalization exhibits a good performance compared to other approximations. In this paper, we show that this solution can be significantly improved by using expectation propagation (EP) in the estimation of the a posteriori probabilities. First, it yields a more accurate estimation of the extrinsic distribution to be sent to the channel decoder. Second, compared to other solutions based on EP, the computational complexity of the proposed solution is constrained to be quadratic in the length of the finite impulse response. In addition, we review the previous EP-based turbo equalization implementations. Instead of considering default uniform priors, we exploit the outputs of the decoder. Some simulation results are included to show that this new EP-based filter remarkably outperforms the turbo approach of the previous versions of the EP algorithm and also improves the LMMSE solution, with and without turbo equalization.