Biometrics Based on Single-Trial EEG

Choi, Ga-Young; Choi, Sooin; Rahmawati, Rahmawati; Lee, Hyung-Tak; Lee, Yun‐Sung; Kim, Seong-Uk; Hwang, Han-Jeong

doi:10.1109/iww-bci.2019.8737254

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sustained progress in these areas will contribute to the creation of goods and services that unquestionably satisfy customers' needs, both implicitly and explicitly. [3] Furthermore, real-time simulation of the dynamics produced by physiologically plausible synthetic neural assemblies can be achieved through the use of field programmable gate arrays (FPGA) and brain-inspired computing .…”

Section: Methodological Challengesmentioning

confidence: 99%

Biometric Identification System Using Eeg Signals: A Review

2024

IRJMETS

View full text Add to dashboard Cite

An overview of current developments in high accuracy EEG biometric user identification is given in this review paper. In addition to highlighting the most recent findings and advancements in this area, the article addresses the prospective of EEG biometrics for trustworthy and secure user identification. The paper also looks at the drawbacks and restrictions of EEG biometrics and considers possible future paths for study and advancement. This paper's overall goal is to give readers a thorough overview of the state of high precision user identification with EEG biometrics today and to provide guidance for future developments in this field.Analyses of brain waves obtained using a consumer-grade EEG equipment explore the device's potential for user authentication and identification. The P300 element of event-related potential (ERP) information gathered from 14-channel-related EEGs on 25 patients is statistically significant, to start. Next, alternative combination of a method for reducing dimensionality and a classification algorithm are used, and their user identification performance is compared using a range of machine learning techniques.

show abstract

Section: Methodological Challengesmentioning

confidence: 99%

Biometric Identification System Using Eeg Signals: A Review

2024

IRJMETS

View full text Add to dashboard Cite

show abstract

“…Poulos, Rangoussi, and Alexandris 103 and Paranjape et al 104 published studies in 1999 and 2001 respectively demonstrating classification systems which could identify individuals from a small sample based on features of their EEG with accuracies between 80 and 100%. Since then a number of studies 15,[105][106][107][108][109] have implemented biometric authentication systems based on resting-state EEG with reasonable success, demonstrating that the uniqueness of individual users' EEGs is sufficient for biometric authentication, at least within a restricted population.…”

Section: Bcis For Authenticationmentioning

confidence: 99%