A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes

Chen, Yiyu; Atnafu, Ayalneh Dessalegn; Schlattner, Isabella; Weldtsadik, Wendimagegn Tariku; Roh, Myung-Cheol; Kim, Hyoung Joong; Lee, Seong–Whan; Blankertz, Benjamin; Fazli, Siamac

doi:10.1109/tifs.2016.2577551

Cited by 163 publications

(84 citation statements)

References 74 publications

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“…Many previous studies on EEG biometric identification have claimed high accuracy results [46] by using single session datasets, e.g. [47], or by randomly selecting training and validation samples regardless of the data acquisition days [48].…”

Section: Protocol P1: Biased Scenariomentioning

confidence: 99%

On the Influence of Affect in EEG-Based Subject Identification

Arnau-González

Arevalillo‐Herráez

Katsigiannis

et al. 2021

IEEE Trans. Affective Comput.

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Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user's current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the signal is subject-related, but the affective state also has a contribution that affects identification accuracy. Results show that identification accuracy increases when the system has been trained with EEG recordings that refer to similar affective states as the sample that is to be identified. This improvement holds independently of the features and classification algorithm used, and it is generally above 10% under a rigorous setting, when the training and validation datasets do not share data from the same recording days. This finding emphasizes the potential benefits of considering affective information in applications that require subject identification, such as user authentication.

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Section: Protocol P1: Biased Scenariomentioning

confidence: 99%

On the Influence of Affect in EEG-Based Subject Identification

Arnau-González

Arevalillo‐Herráez

Katsigiannis

et al. 2021

IEEE Trans. Affective Comput.

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show abstract

“…Third, we exploited wet electrodes, but it is too inconvenient to utilize for the real-life surgical environment. So, we should verify that it is possible using dry electrodes [35]. Finally, we could not guarantee it could explain the new subject.…”

Section: Discussionmentioning

confidence: 94%

Prediction of Individual Propofol Requirements based on Preoperative EEG Signals

Kweon

Lee

Won

et al. 2020

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

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The patient must be given an adequate amount of propofol for safe surgery since overcapacity and low capacity cause accidents. However, the sensitivity of propofol varies from patient to patient, making it very difficult to determine the propofol requirements for anesthesia. This paper aims to propose a neurophysiological predictor of propofol requirements based on the preoperative electroencephalogram (EEG). We exploited the canonical correlation analysis that infers the amount of information on the propofol requirements. The results showed that the preoperative EEG included the factor that could explain the propofol requirements. Specifically, the frontal and posterior regions had crucial information on the propofol requirements. Moreover, there was a significantly different power in the frontal and posterior regions between baseline and unconsciousness periods, unlike the alpha power in the central region. These findings showed the potential that preoperative EEG could predict the propofol requirements.

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“…BCI paradigms are mainly developed to motor imagery [22][23][24][25][26][27], ERP [28][29][30][31][32], and steady-state visual evoked potential (SSVEP) [6,7,[32][33][34]. ERP and SSVEP are visual responses and are widely used to recognize human intention because their patterns in EEG signals are relatively huge and they showed reliable performance when it comes to accuracy and response time with only a few EEG channels comparing to other BCI paradigms.…”

Section: Introductionmentioning

confidence: 99%

Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals

Leel

Lee

2020

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

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Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography signals are distorted by movement artifacts and electromyography signals in ambulatory condition, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer interface and is widely used. However, ear-EEG still contains contaminated signals. In this paper, we proposed robust two-stream deep neural networks in walking conditions and analyzed the visual response EEG signals in the scalp and ear in terms of statistical analysis and braincomputer interface performance. We validated the signals with the visual response paradigm, steady-state visual evoked potential. The brain-computer interface performance deteriorated as 3~14% when walking fast at 1.6 m/s. When applying the proposed method, the accuracies increase 15% in cap-EEG and 7% in ear-EEG. The proposed method shows robust to the ambulatory condition in session dependent and session-to-session experiments.

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A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes

Cited by 163 publications

References 74 publications

On the Influence of Affect in EEG-Based Subject Identification

On the Influence of Affect in EEG-Based Subject Identification

Prediction of Individual Propofol Requirements based on Preoperative EEG Signals

Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals

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