Aphasia is a disorder that affects the ability to speak, write and/or understand language and is caused by an injury or damage to the brain. There is scientific evidence that event related potentials (ERPs) can be used to diagnose aphasia and follow-up the neuroplasticity of the brain during treatment. ERP components are usually quantified in the average ERP of the patient for a specific task. However, it is known that late ERP components, such as the P300, are blurred in the averaged waveform due to inter-trial variability. Therefore, more advanced signal processing techniques to quantify the brain’s response to different language tasks and to create EEG biomarkers, i.e. an ERP fingerprint, will be investigated. Analyzing the differences in this fingerprint between patients with aphasia and healthy controls will allow us to objectively determine which parts of the language network in the brain are damaged. This knowledge can help selecting treatment exercises for the patient that specifically target the underlying problem. The aim of this project is to 1) investigate different EEG signal processing techniques that take into account the inter-trial variability of the ERP components, 2) investigate the differences in ERP fingerprints between patients with aphasia and controls to better understand the neuroplasticity in the brain and 3) automate the processing of the EEG data and extraction of the fingerprint, to allow a more wide-spread use of the EEG approach in the clinic.