Auditory selective attention plays a central role in the human capacity to reliably process complex sounds in multi-source environments. Stimulus reconstruction has been widely used for the investigation of selective auditory attention using multichannel electroencephalography (EEG). In particular, the influence of attention on sound representations in the brain has been modeled by linear time-variant filters and have been used to track the attentional state of individuals in multi-source environments. Detection of auditory attention is of interest and is important in the study of attention-related disorders and has potential application in the hearing aid and advertising industries. In analogy with the rake receiver from wireless communications, we propose a new strategy, adapting principles from minimum variance beamforming, to reconstruct stimuli for decoding the attentional state of listeners in a competing speaker environment. We show through experiments with real electrophysiological data how decoding accuracies can be improved using our proposed scheme.