Emotion State Recognition Using Band Power of EEG Signals

Halim, N. F. A.; Fuad, N.; Marwan, M. E.; Nasir, E. M. N. E. M.

doi:10.1007/978-981-16-8690-0_82

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Researchers have used Emotiv Insight and developed many applications. The study 21 explored emotion recognition using EEG signals, focusing on the identification of happy and sad states through the analysis of brain signal features and power spectral density derived from two EEG channels. The research utilized data from 26 subjects, applying preprocessing and Empirical Mode Decomposition, followed by classification via an artificial neural network with an accuracy of 88.5%.…”

Section: Methodsmentioning

confidence: 99%

SignEEG v1.0 : Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems

Mishra,

Kumar,

Gupta

et al. 2023

Preprint

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Noninvasive electroencephalography (EEG) is a method for measuring electrical brain activity from the surface of the scalp. Recent developments in artificial intelligence accelerate the automatic recognition of brain patterns, allowing more reliable and increasingly faster Brain-Computer interfaces, including biometric applications. Biometric research is also focusing on multimodal systems using EEG along with other modalities. This paper presents a new multimodalSignEEG v1.0dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected withEmotiv InsightandWacom Onesensors, respectively. The multimodal data consists of three paradigms with increasing brain functioning: (i) visualizing a signature image, (ii) doing a signature in mind, and (iii) physically drawing a signature. Extensive experiments have been done in order to provide a solid baseline with machine learning classifiers. We release the raw, pre-processed data and easy-to-follow implementation details.

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Section: Methodsmentioning

confidence: 99%

SignEEG v1.0 : Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems

Mishra,

Kumar,

Gupta

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems

Mishra,

Kumar,

Gupta

et al. 2024

Sci Data

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Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality’s strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG’s resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature’s image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.

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Emotion State Recognition Using Band Power of EEG Signals

Cited by 2 publications

References 10 publications

SignEEG v1.0 : Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems

SignEEG v1.0 : Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems

SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems

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