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.
CITATION STYLE
Zotou, S., Kostopoulos, G. K., & Antonakopoulos, T. A. (2017). Real-time spindles detection for acoustic neurofeedback. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10512 LNAI, pp. 159–168). Springer Verlag. https://doi.org/10.1007/978-3-319-67615-9_14
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