Review on identity feature extraction methods based on electroencephalogram signals / 生物医学工程学杂志

Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.

Bi-modal enhancement effect from combination of visual and auditory stimulus / 国际生物医学工程杂志

Objective To investigate the cognitive difference between uni-modal (V,A) and bi-modal (VA)target stimuli from both vision and audition,and then to study the neural mechanisms of bi-modal enhancement.Methods This experiment adopted a speeded target stimuli detection task, both behavioral and electroencephalographic responses to uni-modal and bi-modal target stimuli which were combined from visual and auditory target stimuli,were recorded from 14 normal subjects using a 64-channel EEG NeuroScan system.The differences of cognitive between uni-modal and bi-modal stimulus were tested from both behavioral (reaction time (RT) and error rate (ER)) and event-related potentials (ERPs) (P2 latency and amplitude,P3 latency and amplitude)data,and the correlation between behavioral and ERPs results were analyzed.Results As a result,the RT,ER and P3 latency has significant difference between uni-modal and bi-modal target stimuli.In addition,there were significant correlation between behavioral data and P3 latency,especially from the RT and P3 latency.Conclusion By comparing the difference between uni-modal and bi-modal from both behavioral and ERPs results,we could reached the conclusion that the neural mechanism of bi-modal target detection was predominant over that of vision and audition uni-modal target detection,the enhancement take place not only involved in early ERP components (such as P1 and N1),but engaged at the late ERP components (such as P2 and P3).