Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings
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.
This paper uses Electroencephalography (EEG) recordings to study the possibility of determining the rhythm that a subject is listening to. The authors employ two different techniques: Stacked denoising autoencoders (SDA), and Convolutional Neural Network (CNN).The authors have generated EEG data through experimentation. 13 subjects have listened to 24 different rhythms. Each EEG recording consists of about 13 channels. Each rhythm was 32 seconds long and EEG sampling frequency was 400Hz. In one experiment, the authors train on the data from EEG signals of one individual and test on EEG data from the same individual. They use about 27 seconds of a 32 second rhythm for training and about 2.5 seconds for testing (exact time depends on rhythm tempo). They show that when trained and tested on a single individual, the accuracy of correctly classifying a rhythm into 24 classes is higher than chance (24.4% vs 4.17%). For one specific individual they can classify the EEG signal with 50.0% accuracy.In another experiment they train using the data from all subjects. In this example the accuracy drops to about 10% (still 4.17% chance). In the case of binary classification (whether East African or Western rhythm) the accuracy of joint classification is 57.1% (where chance is 50%). This shows that it is difficult to have a classification model that is appropriate for all subjects. This phenomenon is understandable and is shown in voice recognition literature as well.Although the dataset and the work are novel. There is little technical contribution in this paper. The paper primarily reports the results of two major algorithms on a new dataset. Although this is valuable on itself, I am not sure if it fits into the criteria of NIPS papers.