Electrical Impedance Tomography (EIT) is a non-invasive imaging technique, which has the potential to expedite the differentiation of ischaemic or haemorrhagic stroke, decreasing the time to treatment. Whilst demonstrated in simulation, there are currently no suitable imaging or classification methods which can be successfully applied to human stroke data. Development of these complex methods is hindered by a lack of quality Multi-Frequency EIT (MFEIT) data. To address this, MFEIT data were collected from 23 stroke patients, and 10 healthy volunteers, as part of a clinical trial in collaboration with the Hyper Acute Stroke Unit (HASU) at University College London Hospital (UCLH). Data were collected at 17 frequencies between 5 Hz and 2 kHz, with 31 current injections, yielding 930 measurements at each frequency. This dataset is the most comprehensive of its kind and enables combined analysis of MFEIT, Electroencephalography (EEG) and Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) data in stroke patients, which can form the basis of future research into stroke classification.