1
|
Tatar AB. Biometric identification system using EEG signals. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07795-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
2
|
Chan HL, Kuo PC, Cheng CY, Chen YS. Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition. Front Neuroinform 2018; 12:66. [PMID: 30356770 PMCID: PMC6189450 DOI: 10.3389/fninf.2018.00066] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 09/10/2018] [Indexed: 12/12/2022] Open
Abstract
The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.
Collapse
Affiliation(s)
- Hui-Ling Chan
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
| | - Po-Chih Kuo
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
| | - Chia-Yi Cheng
- Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, Taiwan
| | - Yong-Sheng Chen
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.,Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Center for Emergent Functional Matter Science, National Chiao Tung University, Hsinchu, Taiwan
| |
Collapse
|
3
|
Rembado I, Castagnola E, Turella L, Ius T, Budai R, Ansaldo A, Angotzi GN, Debertoldi F, Ricci D, Skrap M, Fadiga L. Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro-Electrocorticography. Int J Neural Syst 2016; 27:1650052. [PMID: 27712455 DOI: 10.1142/s0129065716500520] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction - not a negligible phenomenon - is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection. Using well-described cortical responses in SEPs, we were able to validate our results showing that the array could segregate different functional units possessing unique, highly localized spatial distributions. The representation of signals through the root-mean-square (rms) maps and the signal-to-noise ratio (SNR) analysis emphasizes the advantages of adopting a source analysis approach on micro-ECoG recordings in order to obtain a clear picture of cortical activity. The implications are twofold: while on one side ICA may be used as a spatial-temporal filter extracting micro-signal components relevant to tasks for brain-computer interface (BCI) applications, it could also be adopted to accurately identify the sites of nonfunctional regions for clinical purposes.
Collapse
Affiliation(s)
- Irene Rembado
- 1 Center for Translational Neurophysiology IIT@Unife, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, 44121 Ferrara, Italy
| | - Elisa Castagnola
- 1 Center for Translational Neurophysiology IIT@Unife, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, 44121 Ferrara, Italy
| | - Luca Turella
- 2 University of Trento, Center for Mind/Brain Sciences (CIMeC), Via delle Regole, 101, 38123 Trento, Italy
| | - Tamara Ius
- 3 Struttura complessa di Neurochirurgia, Azienda Ospedaliero-Universitaria Santa Maria della Misericordia, Piazzale Santa Maria della Misericordia 15, 33100 Udine, Italy
| | - Riccardo Budai
- 3 Struttura complessa di Neurochirurgia, Azienda Ospedaliero-Universitaria Santa Maria della Misericordia, Piazzale Santa Maria della Misericordia 15, 33100 Udine, Italy
| | - Alberto Ansaldo
- 4 Graphene Labs, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
| | - Gian Nicola Angotzi
- 5 Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Francesco Debertoldi
- 6 Department of Neurosciences and Mental Health, Psychiatric Clinic, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Davide Ricci
- 1 Center for Translational Neurophysiology IIT@Unife, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, 44121 Ferrara, Italy
| | - Miran Skrap
- 3 Struttura complessa di Neurochirurgia, Azienda Ospedaliero-Universitaria Santa Maria della Misericordia, Piazzale Santa Maria della Misericordia 15, 33100 Udine, Italy
| | - Luciano Fadiga
- 1 Center for Translational Neurophysiology IIT@Unife, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, 44121 Ferrara, Italy.,7 Section of Human Physiology, University of Ferrara, Via Fossato di Mortara 17-19, 44121 Ferrara, Italy
| |
Collapse
|
4
|
Chan HL, Chen LF, Chen IT, Chen YS. Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals. Neuroimage 2015; 114:1-17. [DOI: 10.1016/j.neuroimage.2015.03.038] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2013] [Revised: 01/17/2015] [Accepted: 03/14/2015] [Indexed: 11/15/2022] Open
|