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In this work we propose novel decision feedback (DF) detection algorithms with error propagation mitigation capabilities for multi-input multi-output (MIMO) spatial multiplexing systems based on multiple processing branches. The novel strategies for detection exploit different patterns, orderings and constraints for the design of the feedforward and feedback filters. We present constrained minimum mean-squared error (MMSE) filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MIMO receivers and show that the proposed MMSE design does not require a significant additional complexity over the single-branch MMSE design. The proposed multi-branch MMSE DF detectors are compared with several existing detectors and are shown to achieve a performance close to the optimal maximum likelihood detector while requiring significantly lower complexity.