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The processing and analysis of infant EEG presents unique challenges when compared to adult EEG. In particular, artefact is more prevalent in infant EEG, and methods of artefact removal used for adults do not work for infants due to the small numbers of electrodes. We are developing software which provides a single ‘pipeline’ of methods which can reproducibly process EEG data, including a novel machine-learning technique for identifying sections of EEG which contain artefact.

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