Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain-Computer Interface Application.
Nicole ChiouMehmet GünalOluwasanmi O KoyejoDavid PerpetuiniAntonio Maria ChiarelliKathy A LowMonica FabianiGabriele GrattonPublished in: Bioengineering (Basel, Switzerland) (2024)
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.
Keyphrases
- deep learning
- convolutional neural network
- phase iii
- machine learning
- study protocol
- artificial intelligence
- phase ii
- big data
- clinical trial
- electronic health record
- high density
- resting state
- randomized controlled trial
- white matter
- high resolution
- functional connectivity
- open label
- cerebral ischemia
- data analysis
- single molecule
- air pollution
- high speed
- subarachnoid hemorrhage
- double blind
- mass spectrometry
- decision making