In this work we introduce a new algorithm to correct the imaging artefacts in the EEG signal measured during fMRI acquisition. The correction techniques proposed so far cannot optimally represent transitions, i.e. when abrupt changes of the artefact properties due to head movements occur. The algorithm developed here takes the head movement parameters from the fMRI signal into account to calculate adequate EEG artefact templates and subsequently correct the distorted EEG data. The data reported in this work demonstrate that the realignment parameter-informed algorithm outperforms the commonly used moving average algorithm if head movements occur. The superiority is reflected by comparing the residual variance after artefact correction with either method. The residual variance is lower around head-movements that exceed head deflections of about 1 mm when applying the realignment parameter-informed algorithm. Additionally, the signal to noise ratio of a surrogate event-related potential (ERP) increased by 10-40% for head displacements larger than 1 mm. The algorithm developed here is particularly suited for studies where head movements of the subject cannot be prevented as in studies with patients, children, or during sleep. Furthermore, the enhanced signal to noise ratio of a single trial ERP indicates the power of the presented algorithm for single trial ERP-fMRI studies in which EEG signal quality is a critical factor.