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Data-driven user feedback: An improved neurofeedback strategy considering individual variability of EEG features

The aim of the present study was to develop a new neurofeedback strategy named the data-driven user feedback that considers individual variability of electroencephalography (EEG) features in order to make the users of the neurofeedback system experience wider range of feedbacks. Twenty healthy subjects performed a hidden catch paradigm, during which EEG signals were acquired from two prefrontal channels. From our experimental results, 72% increment in the number of valid (feedback) bins could be attained using the proposed strategy.