1
|
Ghani U, Jochumsen M, Gyldenvang MB, Niazi IK. Can water-based EEG caps record robust movement-related cortical potentials (MRCPs) for single and multiple joint movements? ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083438 DOI: 10.1109/embc40787.2023.10340665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Movement-related cortical potentials (MRCPs) have been used extensively in the literature to develop rehabilitation interventions for people with neurological conditions. In this pilot study, we recorded and extracted MRCPs using a water-based cap to determine whether water-based caps are effective. Five participants took part in the study, where their EEG was recorded during single-joint (dorsiflexion) and multiple-joint (sit-to-stand) lower limb movements. We were able to see clear MRCPs for both movement types with an average peak negativity (PN) latency of +22ms for dorsiflexion and +218ms for sit-to-stand. Similarly, the PN amplitude of -14.89μV was recorded for dorsiflexion and -43.54μV for sit-to-stand. These values were comparable to the values reported in studies using gel-based caps. Based on these results, water-based caps can be an effective way to produce robust MRCPs, which can have many advantages over gel-based caps.Clinical Relevance- The study provides clinicians with a more viable method of collecting EEGs and extracting MRCPs, thus allowing them to design more robust interventions for people with neurological disorders.
Collapse
|
2
|
Sciaraffa N, Di Flumeri G, Germano D, Giorgi A, Di Florio A, Borghini G, Vozzi A, Ronca V, Babiloni F, Aricò P. Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces. Front Hum Neurosci 2022; 16:901387. [PMID: 35911603 PMCID: PMC9331459 DOI: 10.3389/fnhum.2022.901387] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022] Open
Abstract
Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.
Collapse
Affiliation(s)
| | - Gianluca Di Flumeri
- BrainSigns Srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | | | - Andrea Giorgi
- BrainSigns Srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | | | - Gianluca Borghini
- BrainSigns Srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Alessia Vozzi
- BrainSigns Srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Vincenzo Ronca
- BrainSigns Srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Fabio Babiloni
- BrainSigns Srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Pietro Aricò
- BrainSigns Srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy
| |
Collapse
|