Emotiv Insight with Convolutional Neural Network: Visual Attention Test Classification

Toa, Chean Khim; Sim, K. S.; Tan, Shing Chiang

doi:10.1007/978-3-030-88113-9_28

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Toa et al 27 and Heunis 28 have also used ICA on Emotiv Insight data to remove the noise from the EEG signals. By applying ICA, the components associated with the artifacts, as well as the brain activity, are identified.…”

Section: Technical Validationmentioning

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: Technical Validationmentioning

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

“…Hence, there are some minimum requirements in the smartphone hardware specifications in order to run the Augmented Reality application on the smartphone to attain high-quality interaction [7]. The minimum requirement of the smartphone to run the AR application is: 2GHz microprocessor, 4GB random access memory (RAM), and 64 GB read-only memory (ROM), As the technology of mobile phones advances, all the midrange smartphones are equipped with the hardware that fulfills the minimum requirements [8]. Hence, AR applications for mobile phones are getting popular as they can be operated on most of the smartphones.…”

Section: Introductionmentioning

confidence: 99%

Development of Augmented Reality Based Applications for Brain Memory Training

Lim

Toa

Rao

et al. 2023

IJORAS

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This paper presents the development of augmented reality (AR) based brain memory training to improve the memorizing capability of the student. The AR visual memory training application is built on top of the mobile phone by utilizing the Unity and Vuforia platform. The developed visual memory test is a flipping card test that can measure a person’s memory capability to retain visual images and spatial perception in the mind. In this study, it is aimed to prove that AR technology is suitable to be employed in the education field. The results are justified based on the visual memory test score and the engaging level of the user computed from the electroencephalogram (EEG) signal. The results are assessed by comparing with the physical mode and computer-based mode. As result, it is shown that the student performed better in the AR-based visual memory test compared to physical and computer-based modes. Besides, the EEG signals also show that students are more engaged and attentive while using AR technology.

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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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Emotiv Insight with Convolutional Neural Network: Visual Attention Test Classification

Cited by 3 publications

References 13 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

Development of Augmented Reality Based Applications for Brain Memory Training

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

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