Brainprint based on functional connectivity and asymmetry indices of brain regions: A case study of biometric person identification with non‐expensive electroencephalogram headsets

Abstract

Abstract Brain‐computer interface applications for biometric person identification have increased their interest in recent years since they are potentially more secure and more difficult to counterfeit than traditional biometric techniques. However, it is necessary to consider how brain waves are acquired for this purpose, not only in terms of efficiency but also of practical comfort for the user and the affordability degree of the biosignal acquisition device so that their everyday application can become a realistic possibility. In this context, this paper presents the capabilities of using a non‐expensive wireless electroencephalogram (EEG) device to extract spectral‐related and functional connectivity information of brain activity. The proposed method achieved a sufficient biometric identification with two datasets of 13 and 109 subjects when comparing the performance of a sizeable classification algorithm set. In addition, a novel feature in EEG biometric identification, called asymmetry index, is introduced here. Furthermore, this is the first study in this field to consider the effect of the time‐lapse between different recording sessions on the system's behaviour when using a low‐cost EEG device with identification accuracy rates of up to 100%.

Keywords

• biometrics brain‐computer interface (BCI)
• classification
• electroencephalogram (EEG)