Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform
Document Type
Conference Proceeding
Date of Original Version
1-1-2024
Abstract
There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cortices - to grasp execution - dominated by the motor cortices. In this study, we developed a novel vision-based-grasping BCI platform that distinguishes different grip types (power and precision) through the phases of plan-to-grasp tasks using EEG signals. Using our platform and extracting features from Filter Bank Common Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains discriminative spatial patterns present in both the observation and movement phases. Support Vector Machine (SVM) classification (power vs precision) yielded high accuracy percentages of 74% and 68% for the observation and movement phases in the alpha band, respectively.
Publication Title, e.g., Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS
Citation/Publisher Attribution
Cetera, Anna, Ali Rabiee, Sima Ghafoori, Yalda Shahriari, and Reza Abiri. "Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform." Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS (2024). doi: 10.1109/EMBC53108.2024.10782523.