Neuro-guided speaker extraction, i.e. NeuroSpex, aims to isolate the speech signal a listener is attending to in a multi-talker environment using reference cues derived from cortical activity, such as electroencephalography (EEG). Despite remarkable progress, there remains untapped potential. In this study, we propose NeuroSpex+, a novel neuro-guided speaker extraction model that integrates an additional task of reconstructing the target speech envelope. By simultaneously optimizing the model for both the target speech envelope and speech waveform, NeuroSpex+ reinforces the mask generation for speaker extraction. Experimental results demonstrate that the proposed model significantly outperforms baselines, improving overall signal quality.