RESEARCH
Active BCI
This project intends to fill the gap between the human brain and external devices in the framework of motor-imagery (MI)-based brain computer interfaces (BCIs) technologies. The BCI is a system able to read voluntary changes in brain activity and then translate neural signals into a message or a computational command in real-time. Among the various types of systems, a non-invasive BCI based on electroencephalography (EEG) and MI transforms neuroelectric signals derived from motor regions into command outputs for external effectors. The success of translating the signal in messages depends on several methodological contributing factors, i.e. decoding algorithms, calibrated on the motor regions using a suitable representation of the data that simplifies the classification or detection of specific brain patterns. The state-of-the-art devices provide good performance albeit limited, when few EEG electrodes are used, calling for novel approaches. In answer to this call, we propose a new method for extracting discriminative features allowing to distinguish different mental movements within a multivariate brain connectivity and deep learning framework. The system is trained to decode the brain activity during MI tasks using the coupling information or causal influence between signals and will produce corresponding signals to an interface that controls an external device.
Passive BCI allows the monitoring of a subject’s mental state, without the need of any active input. It has applications in fields where operator mistakes may cause severe accidents, such as air traffic control, plant surveillance, or driving. It can also be applied in the manufactory context, specifically by monitoring an operator’s level of vigilance, which can be defined as the capacity to sustain one’s attention during the realization of a task, and their mental workload, that is the effort furnished to respond to the task's demands. Both vigilance and mental workload fluctuations can be observed over time by analyzing the EEG, since they are associated with specific behaviors. The goal of the project is to create a deep learning pipeline capable of monitoring an operator’s mental state in real-time, during the execution of prolonged activities. The model needs to alert operators by audio or video cues, should they become too tired to work without endangering themselves or the assembly procedure.
Publications
Siviero I., Menegaz G. and Storti S.F.
"Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance"
Sensors, 2023
https://www.mdpi.com/1424-8220/23/17/7520
Siviero I., Brusini L., Menegaz G. and Storti S.F.
"Motor-imagery EEG signal decoding using multichannel-empirical wavelet transform for brain computer interfaces"
2022 IEEE-EMBS International Conference on Biomedical and Health Informatics.
https://ieeexplore.ieee.org/abstract/document/9926766
Stival F., Setti F., Menegaz G., Storti S.F.
"Connectivity Modeling Meets Machine Learning: The Next Generation of EEG-Based Brain Computer Interfaces."
10th International IEEE/EMBS Conference on Neural Engineering (NER), Virtual Conference, May 4-6, 2021.
https://ieeexplore.ieee.org/abstract/document/9441440
Brusini L., Stival F., Setti F., Menegatti E., Menegaz. G, & Storti, S.F.
"A Systematic Review on Motor-Imagery Brain-Connectivity-Based Computer Interfaces",
IEEE Transactions on Human-Machine Systems, 51(6): 725-733, Dec 2021. doi: 10.1109/THMS.2021.3115094.
https://ieeexplore.ieee.org/document/9578928
Di Flumeri G., Aricò P., Borghini G., Sciaraffa N., Ronca V., Vozzi A., Storti S.F., Menegaz G., Fiorini P., Babiloni F.
"EEG-Based Workload Index as a Taxonomic Tool to Evaluate the Similarity of Different Robot-Assisted Surgery Systems."
In: Longo L., Leva M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2019. Communications in Computer and Information Science, Print ISBN: 978-3- 030-32422-3, Volume 1107. Springer, Cham.
OPERA 4.0.: “Osservazione dei processi lavorativi, con garanzie oggettive di tutela della Privacy, per la prevenzione di Errori e situazioni di Rischio in maniera Automatica ai tempi di Industria 4.0 (OPERA 4.0)”, PI: Prof. D. Quaglia. Department of Computer Science (University of Verona), duration: 24 months, e100K. Role: participant - Work Package: MA2 Intelligent systems.
https://www.di.univr.it/?ent=progetto&id=5557
EBNeuro S.p.A - Dr. Marco Rossi http://www.ebneuro.com/en/
2019 Best Paper Award: "EEG-Based Workload Index as a Taxonomic Tool to Evaluate the Similarity of Different Robot-Assisted Surgery Systems" at H-WORKLOAD 2019, Nov 14-15, Rome, Italy. http://hworkload.org/2019/awards/