title: EEG biometric identification: a thorough exploration of the time-frequency domain.
abstract: OBJECTIVE
  Although interest in using electroencephalogram (EEG) activity for subject identification has grown in recent years, the state of the art still lacks a comprehensive exploration of the discriminant information within it. This work aims to fill this gap, and in particular, it focuses on the time-frequency representation of the EEG.
  
  
  METHODS
  We executed qualitative and quantitative analyses of six publicly available data sets following a sequential experimentation approach. This approach was divided in three blocks analysing the configuration of the power spectrum density, the representation of the data and the properties of the discriminant information. A total of ten experiments were applied.
  
  
  RESULTS
  Results show that EEG information below 40 Hz is unique enough to discriminate across subjects (a maximum of 100 subjects were evaluated here), regardless of the recorded cognitive task or the sensor location. Moreover, the discriminative power of rhythms follows a W-like shape between 1 and 40 Hz, with the central peak located at the posterior rhythm (around 10 Hz). This information is maximized with segments of around 2 s, and it proved to be moderately constant across montages and time.
  
  
  CONCLUSIONS
  Therefore, we characterize how EEG activity differs across individuals and detail the optimal conditions to detect subject-specific information. This work helps to clarify the results of previous studies and to solve some unanswered questions. Ultimately, it will serve as guide for the design of future biometric systems.
authors.affiliation: University Institute for Technical Development and Innovation in Communication (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Spain. Department of Psychology, College of Human Health and Science, Swansea University, Wales, UK.
authors.forename: Marcos
authors.initials: M
authors.lastname: DelPozo-Banos
authors.forename: Carlos M
authors.initials: CM
authors.lastname: Travieso
authors.forename: Christoph T
authors.initials: CT
authors.lastname: Weidemann
authors.forename: Jesús B
authors.initials: JB
authors.lastname: Alonso
mesh_terms: D000328:Adult; D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult