On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Yang, Su; Deravi, Farzin

doi:10.1109/thms.2017.2682115

Cited by 75 publications

(70 citation statements)

References 65 publications

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“…Results reported in Tables 3 and 4 reveal that accuracy results are considerably lower when using the rigorous scenario, as also highlighted in the box plots of Figure 6. This finding is consistent with the bias reported in [32] that leads to unrealistically high results when using protocol P1, and also with the phenomenon of Template Aging which effects when using recordings of cognitive tasks performed with long time intervals between them, as discussed in [25], [52], and [53]. However, results from both cases support this work's argument that the emotional state of the individual affects identification accuracy.…”

Section: Results On Rigorous Scenariosupporting

confidence: 89%

“…The theoretical aspects and future perspectives of EEG-biometric systems have been reviewed in [3] and [24]. Another survey, more focused on practicality and usability issues has been presented in [25]. This includes signal acquisition devices, with medical-grade sensors and low-cost devices been used in this context.…”

Section: State-of-the-artmentioning

confidence: 99%

“…In some cases, it may be appropriate to involve the user into a concrete task, or to use a predefined stimulus to evoke the desired brain activity. In this regard, several approaches have been reported in the literature, mainly covering three types of brain stimulation: resting state, sensory (audio/visual) stimuli, and cognitive tasks-(verbal instructions) [25]. The first two have predominantly been used for subject identification, where the goal is to match the identity of a subject out of a closed pool of people.…”

Section: State-of-the-artmentioning

confidence: 99%

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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: Results On Rigorous Scenariosupporting

confidence: 89%

Section: State-of-the-artmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

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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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“…The P 300 wave is a type of Event-Related Potential (ERP) that is believed to be dominant in decision-making [42], and is usually within the range of 250ms to 500ms of the onset of visual stimulus [43]. Similar to the P 300 wave, Simple Reaction Time (SRT) represents the delay between visual stimulus and response, during which the usage of the sensor pairs at t " 0.25s and t " 0.50s shows brain activity.…”

Section: Methodsmentioning

confidence: 99%

Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

et al. 2020

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Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging from human-computer interaction to medical and biomedical applications. This paper proposes a novel solution for classification of left/right hand movement by exploiting a Long Short-Term Memory (LSTM) network with attention mechanism to learn the electroencephalogram (EEG) time-series information. To this end, a wide range of time and frequency domain features are extracted from the EEG signals and used to train an LSTM network to perform the classification task. We conduct extensive experiments with the EEG Movement dataset and show that our proposed solution our method achieves improvements over several benchmarks and state-of-the-art methods in both intrasubject and cross-subject validation schemes. Moreover, we utilize the proposed framework to analyze the information as received by the sensors and monitor the activated regions of the brain by tracking EEG topography throughout the experiments.

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“…images or video). Ruiz-Blondet et al [8] and Maiorana and Campisi [9] examined the use of various tasks in order to achieve brain stimulation, typically resting state, audiovisual stimuli (sensory activities), or cognitive tasks, as commonly proposed in the literature [10]. Imagined speech [11] and custom tasks [12], as well as the use of event-related potentials (ERPs) [13] have also been employed and led to increased subject identification accuracy and stability over time (enhanced permanence).…”

Section: Introductionmentioning

confidence: 99%

Image-Evoked Affect and its Impact on Eeg-Based Biometrics

Arnau-González

Katsigiannis

Arevalillo‐Herráez

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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Electroencephalography (EEG) signals provide a representation of the brain's activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual's emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordings that refer to similar emotional states as the EEG recordings that are used for identification, demonstrating an up to 5.3% increase on identification accuracy compared to when recordings referring to different emotional states are used. Furthermore, this improvement holds independently of the features and classification algorithms employed.

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On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 75 publications

References 65 publications

On the Influence of Affect in EEG-Based Subject Identification

On the Influence of Affect in EEG-Based Subject Identification

Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

Image-Evoked Affect and its Impact on Eeg-Based Biometrics

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