Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain–Computer Interface Application

Abstract

Event-related optical signals (EROS) measure fast modulations in the brain’s optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain–computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications.

Keywords

• fast optical signals (FOS)
• event-related optical signals (EROS)
• brain–computer interface (BCI)
• machine learning (ML)
• deep learning