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.
@article{arxiv.2402.03493,
title = {Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform},
author = {Anna Cetera and Ali Rabiee and Sima Ghafoori and Reza Abiri},
journal= {arXiv preprint arXiv:2402.03493},
year = {2024}
}