A Single Subject Machine Learning Based Classification of Motor Imagery EEGs

Motor Imagery-Based Brain-Computer Interfaces (MI-BCIs) are systems that detect and interpret brain activity patterns linked to the mental visualization of movement, and then translate these into instructions for controlling external robotic or domotic devices. Such devices have the potential to be useful in a broad variety of applications. While implementing a system that would help individuals restore some freedom levels, the interpretation of (Electroencephalography) EEG data remains a complex and unsolved problem. In the literature, the classification of left and right imagined movements has been extensively studied. This study introduces a novel pipeline that makes use of machine learning techniques for classifying MI EEG data. The entire framework is capable of accurately categorizing left and imagined motions, as well as rest phases, for a set of 52 subjects who performed a MI task. We trained a within subject model on each individual subject. The methodology has been offline evaluated and compared to four studies that are currently the state-of-the-art regarding the specified dataset. The results show that our proposed framework could be used with MI-BCI systems in light of its failsafe classification performances, i.e. 99.5% in accuracy

Single-cell phase-contrast tomograms data encoded by 3D Zernike descriptors

Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and management of the huge amount of 3D tomographic data. Here we show an effective quasi lossless compression of tomograms data through 3D Zernike descriptors, unlocking data management tasks and computational pipelines that were unattainable until now.