Real-time Spindles Detection for Acoustic Neurofeedback

Stella Zotou, George K. Kostopoulos, Theodore A. Antonakopoulos

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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