COMPARISON OF CNN-BASED EEG CLASSIFICATION IN SENSOR AND SOURCE SPACE

Electroencephalography (EEG) is a popular tool in brain-computer interfacing (BCI), due to its unique time resolution and simplicity of application. For the design of BCIs, rapid and accurate classification algorithms are needed to classify the brain state correctly in real-time. Recent technological advancements facilitate the use of novel methods for signal processing and analysis such as real-time source estimation and classification via deep learning approaches. In this work a previously established convolutional neural network (CNN) architecture, the EEGNet, was applied to a publicly available motor imagery EEG dataset for classification of sensor measurements and source estimates that were computed with three different inverse approaches. Both for sensor signals and source estimates similar classification accuracies as in the literature could be achieved. However, no significant difference in performance between sensor and source space analysis was observed.