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Mascia A, Collu R, Spanu A, Fraschini M, Barbaro M, Cosseddu P. Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording. SENSORS (BASEL, SWITZERLAND) 2023; 23:766. [PMID: 36679563 PMCID: PMC9861766 DOI: 10.3390/s23020766] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
In an increasingly interconnected world, where electronic devices permeate every aspect of our lives, wearable systems aimed at monitoring physiological signals are rapidly taking over the sport and fitness domain, as well as biomedical fields such as rehabilitation and prosthetics. With the intent of providing a novel approach to the field, in this paper we discuss the development of a wearable system for the acquisition of EEG signals based on a portable, low-power custom PCB specifically designed to be used in combination with non-conventional ultra-conformable and imperceptible Parylene-C tattoo electrodes. The proposed system has been tested in a standard rest-state experiment, and its performance in terms of discrimination of two different states has been compared to that of a commercial wearable device for EEG signal acquisition (i.e., the Muse headset), showing comparable results. This first preliminary validation demonstrates the possibility of conveniently employing ultra-conformable tattoo-electrodes integrated portable systems for the unobtrusive acquisition of brain activity.
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Affiliation(s)
- Antonello Mascia
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Riccardo Collu
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Andrea Spanu
- Department of Science, Technology and Society, Scuola Universitaria Superiore IUSS, Palazzo del Broletto, Piazza della Vittoria 15, 27100 Pavia, Italy
| | - Matteo Fraschini
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Massimo Barbaro
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
| | - Piero Cosseddu
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D’Armi, 09123 Cagliari, Italy
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3
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Hommelsen M, Viswanathan S, Daun S. Robustness of individualized inferences from longitudinal resting state EEG dynamics. Eur J Neurosci 2022; 56:3613-3644. [PMID: 35445438 DOI: 10.1111/ejn.15673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 03/21/2022] [Accepted: 04/08/2022] [Indexed: 11/27/2022]
Abstract
Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification. We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-EEG was acquired over five consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on one day were analyzed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision-rule, an individual could be re-identified from 2-second samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded-RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day vs same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of longitudinal RS for robust individualized inferences about neurophysiological change in health and disease.
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Affiliation(s)
- Maximilian Hommelsen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
| | | | - Silvia Daun
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany.,Institute of Zoology, University of Cologne, Cologne, Germany
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4
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Puxeddu MG, Petti M, Astolfi L. Multi-layer analysis of multi-frequency brain networks as a new tool to study EEG topological organization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:924-927. [PMID: 34891441 DOI: 10.1109/embc46164.2021.9630173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Oscillatory activity rising from the interaction among neurons is widely observed in the brain at different scales and is thought to encode distinctive properties of the neural processing. Classical investigations of neuroelectrical activity and connectivity usually focus on specific frequency bands, considered as separate aspects of brain functioning. However, this might not paint the whole picture, preventing to see the brain activity as a whole, as the result of an integrated process. This study aims to provide a new framework for the analysis of the functional interaction between brain regions across frequencies and different subjects. We ground our work on the latest advances in graph theory, exploiting multi-layer community detection. In our multi-layer network model, layers keep track of single frequencies, including all the information in a unique graph. Community detection is then applied by means of a multilayer formulation of modularity. As a proof-of-concept of our approach, we provide here an application to multi-frequency functional brain networks derived from resting state EEG collected in a group of healthy subjects. Our results indicate that α-band selectively characterizes an inter-individual common organization of EEG brain networks during open eyes resting state. Future applications of this new approach may include the extraction of subject-specific features able to capture selected properties of the brain processes, related to physiological or pathological conditions.
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Orkan Olcay B, Özgören M, Karaçalı B. On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels. Neural Netw 2021; 143:452-474. [PMID: 34273721 DOI: 10.1016/j.neunet.2021.06.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/04/2021] [Accepted: 06/18/2021] [Indexed: 10/21/2022]
Abstract
Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Δt, the time lag between maximally synchronized signal segments τ, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the inter-channel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes.
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Affiliation(s)
- B Orkan Olcay
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
| | - Murat Özgören
- Department of Biophysics, Faculty of Medicine, Near East University, 99138, Nicosia, Cyprus.
| | - Bilge Karaçalı
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
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6
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Solis I, Janowich J, Candelaria-Cook F, Collishaw W, Wang YP, Wilson TW, Calhoun VD, Ciesielski KRT, Stephen JM. Frontoparietal network and neuropsychological measures in typically developing children. Neuropsychologia 2021; 159:107914. [PMID: 34119500 DOI: 10.1016/j.neuropsychologia.2021.107914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022]
Abstract
Resting-state activity has been used to gain a broader understanding of typical and aberrant developmental changes. However, the developmental trajectory of resting-state activity in relation to cognitive performance has not been studied in detail. The present study assessed spectral characteristics of theta (5-8 Hz) and alpha (9-13 Hz) frequency bands during resting-state in a priori selected regions of the frontoparietal network (FPN). We also examined the relationship between resting-state activity and cognitive performance in typically developing children. We hypothesized that older children and children with high attentional scores would have higher parietal alpha activity and frontal theta activity while at rest compared to young children and those with lower attentional scores. MEG data were collected in 65 typically developing children, ages 9-14 years, as part of the Developmental Chronnecto-Genomics study. Resting-state data were collected during eyes open and eyes closed for 5 min. Participants completed the NIH Toolbox Flanker Inhibitory Control (FICA) and Attention Test and Dimensional Change Card Sort Test (DCCS) to assess top-down attentional control. Spectral power density was used to characterize the FPN. We found during eyes open and eyes closed, all participants had higher theta and alpha power in parietal regions relative to frontal regions. The group with high attentional scores had higher alpha power during resting-state eyes closed compared to those with low attentional scores. However, there were no significant differences between age groups, suggesting changes in the maturation of neural oscillations in theta and alpha are not evident among children in the 9-14-year age range.
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Affiliation(s)
- Isabel Solis
- Department of Psychology, University of New Mexico, 2001 Redondo S Dr, Albuquerque, NM, 87106, USA; Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - Jacki Janowich
- Department of Psychology, University of New Mexico, 2001 Redondo S Dr, Albuquerque, NM, 87106, USA; Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - Felicha Candelaria-Cook
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - William Collishaw
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, 6823 St. Charles Ave, New Orleans, LA, 70118, USA.
| | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center, 988440 Nebraska Medical Center, Omaha, NE, 68198, USA.
| | - Vince D Calhoun
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, 498 Terrace St NE, Albuquerque, NM, 87106, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 33 Glimer St SE, Atlanta, GA, 30303, USA.
| | - Kristina R T Ciesielski
- Department of Psychology, University of New Mexico, 2001 Redondo S Dr, Albuquerque, NM, 87106, USA; MGH/MIT A. A. Martinos Center for Biomed. Imaging, Dept of Radiology, Harvard Medical School, 149 Thirteenth St, Suite 2301, Charleston, MA, 02129, USA.
| | - Julia M Stephen
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
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7
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Fraschini M, Meli M, Demuru M, Didaci L, Barberini L. EEG Fingerprints under Naturalistic Viewing Using a Portable Device. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6565. [PMID: 33212929 PMCID: PMC7698321 DOI: 10.3390/s20226565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 11/13/2020] [Accepted: 11/16/2020] [Indexed: 06/11/2023]
Abstract
The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.
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Affiliation(s)
- Matteo Fraschini
- Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy; (M.M.); (L.D.)
| | - Miro Meli
- Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy; (M.M.); (L.D.)
| | - Matteo Demuru
- Stichting Epilepsie Instellingen Nederland (SEIN), 2103SW Heemstede, The Netherlands;
| | - Luca Didaci
- Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy; (M.M.); (L.D.)
| | - Luigi Barberini
- Department of Medical Sciences and Public Health, University of Cagliari, 09123 Cagliari, Italy;
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