EEG Authentication
Traditional authentication models are vulnerable to security breaches when personal data is exposed. This study introduces novel hybrid visual stimuli protocols integrating event-related potentials (ERP) and steady-state visually evoked potentials (SSVEP) to develop an authentication system that enhances both performance and personalization in neural interfaces. Our model utilizes distinctive neural patterns elicited by a range of visual stimuli based on 4-digit numbers, such as familiar numbers (personal birthdates, excluding targets), standard targets, and non-targets.
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This dataset is a collection of brainwave EEG signals from eight subjects. The data is collected in a lab controlled environment under a specific visualization experiment.
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BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019).
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