Abstract
Real-time neurofeedback plays an increasing role in today’s clinical and basic neuroscience research. In this work, we present a real-time sleep EEG spindles detection algorithm fast enough to be used for real time acoustic feedback stimulation. We further highlight the architecture of a system that implements the algorithm and its experimental evaluation. This system can handle EEG data acquired by various means (i.e. conventional EEG systems, wireless sensors) and a response time of a few msecs has been achieved. The presented algorithm is dynamically adaptive and has accuracy similar to other well-known non real-time algorithms. Comparison and evaluation was performed using EEG data from an open database.
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Zotou, S., Kostopoulos, G.K., Antonakopoulos, T.A. (2017). Real-time Spindles Detection for Acoustic Neurofeedback. In: Frasson, C., Kostopoulos, G. (eds) Brain Function Assessment in Learning. BFAL 2017. Lecture Notes in Computer Science(), vol 10512. Springer, Cham. https://doi.org/10.1007/978-3-319-67615-9_14
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DOI: https://doi.org/10.1007/978-3-319-67615-9_14
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