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Detailed Power Spectrum Analysis to Distinguish Motor and Executed EEG Signals

Goal: Main goal is detecting subject-specific EEG bands that are related to imagery.

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Apdullah Yayık
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Deep learning with convolutional neural networks (ConvNets) have dramatically improved learning capabilities of computer vision applications just through considering raw data without any prior feature extraction. Nowadays, there is rising curiosity in interpreting and analyzing electroen-cephalography (EEG) dynamics with ConvNets. Our study focused on ConvNets of different structures, constructed for predicting imagined left and right movements on a subject-independent basis through raw EEG data. Results showed that recently advanced methods in machine learning field, i.e. adaptive moments and batch normalization together with dropout strategy, improved ConvNets predicting ability, out-performing that of conventional fully-connected neural networks with widely-used spectral features.
Apdullah Yayık
added a project goal
Main goal is detecting subject-specific EEG bands that are related to imagery.