Maria V. Ruiz-Blondet

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BACKGROUND Active amplification electrodes are becoming more popular for ERP data collection, as they amplify the EEG at the scalp and thereby potentially decrease the influence of ambient electrical noise. However, the performance of active electrodes has not been directly compared with that of passive electrodes in the context of collecting ERPs from a(More)
Can a person be identified uniquely by some feature of their neural activity, as they can be by fingerprints? If so, 1) what would those features be like and 2) are existing computational methods sufficient to extract them? Here, we explore these questions by coordinating psychophysiological and machine learning approaches. We begin with the proposition(More)
Brainprint biometrics, as an emerging biometric technology, have recently gained increasing attention based on the assumption that each individual has unique memory and knowledge that are capable of providing distinctness from others. Like all other biometric methods, adversaries can also circumvent and compromise brainprint biometric systems, for example,(More)
Electrical brain activities can be measured noninvasively using electroencephalogram (EEG). This electric signal changes for different tasks, and also changes from subject to subject. Previous studies have shown that the EEG signal is unique enough to be used as a biometric characteristic. However, it is well known that the brain activity can change(More)
EEG brainwaves have recently emerged as a promising biometric that can be used for individual identification, since those signals are confidential, sensitive, and hard to steal and replicate. In this study, we propose a new stimuli-driven, non-volitional brain responses based framework towards individual identification. The non-volitional mechanism provides(More)
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