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Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings
Sebastian Stober · Daniel J Cameron · Jessica A Grahn

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D #None

Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. We apply convolutional neural networks (CNNs) to analyze and classify EEG data recorded within a rhythm perception study in Kigali, Rwanda which comprises 12 East African and 12 Western rhythmic stimuli – each presented in a loop for 32 seconds to 13 participants. We investigate the impact of the data representation and the pre-processing steps for this classification tasks and compare different network structures. Using CNNs, we are able to recognize individual rhythms from the EEG with a mean classification accuracy of 24.4% (chance level 4.17%) over all subjects by looking at less than three seconds from a single channel. Aggregating predictions for multiple channels, a mean accuracy of up to 50% can be achieved for individual subjects.

Author Information

Sebastian Stober (Western University)
Daniel J Cameron
Jessica A Grahn