Eroglu G.Aydin S.Cetin M.Balcisoy S.2024-03-132024-03-1320189781538615010https://doi.org/10.1109/SIU.2018.8404711https://hdl.handle.net/20.500.12662/2864Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780AutoTrainBrain is a neurofeedback and multi-sensory based mobile phone software application, designed in Sabanci University laboratory with the aim of improving the cognitive functions of dyslexic children. It reads electroencephalography (EEG) signals from 14 channels of eMotiv EPOC+ and processes these signals to provide neurofeedback to child for improving the brain signals with visual and auditory cues in real time. AutoTrainBrain software has been applied to a 14-year old dyslexic child, 10 minutes per week for 9 consecutive weeks. The EEG data has been analyzed by using the following three approaches: estimation of single-channel EEG complexity levels (entropy), spectral brain connectivity between two-channels (coherence), single channel relative Alpha band power ratio. Our experimental analysis shows that the proposed brain training system offers improvements based on the measures used in the three approaches mentioned above. This suggests such training may help increase the number of active cortical neurons and improve regional brain connectivity. © 2018 IEEE.eninfo:eu-repo/semantics/openAccessDyslexiaEEG signal processingMulti sensory learningNeurofeedbackConference Object10.1109/SIU.2018.84047112-s2.0-850508269584N/A1