Classification of EEG signal using EACA based approach at SSVEP-BCI

Ashwini, S R; Nagaraj, H. C.

doi:10.11591/ijai.v10.i3.pp717-726

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Figure 1 is a block diagram of the approach framework chosen and redeveloped from research in [29,[38][39][40][41][42][43][44][45]. Data acquisition, followed by baseline correction and denoising in pre-processing, fiducials detection in feature extraction, and parameter assessment in the classification block stage, provides the foundation for identity authentication in this study.…”

Section: Methodsmentioning

confidence: 99%

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2024

ARASET

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Biomedical signals, such as an electrocardiogram (ECG), have been included in wearable platforms for biometric reasons due to the rapid expansion of apps and technology capable of gathering this physiological data. Most studies using ECG biometrics are conducted in clinical settings, which is impractical for wearable ECG-based biometric applications. Therefore, this study aims to determine the reliability of ECG signals obtained from the commercially available Hexoskin Proshirt and HeartIn Fit shirt, which may be worn for biometric verification in real-world scenarios. ECG data from 22 participants were collected over a span period of more than 30 days. The raw ECG signal is first pre-processed in the time domain using noise-removal Butterworth filters, and then a successful QRS segmented feature extraction method is used. Not to mention, 300 datasets were used to test the recommended recognition method using a Quadratic Support Vector Machine (QSVM). In comparison, around 854 datasets were prepared for training and validation of the classifier. The findings showed that the proposed method provided a considerable accuracy above 99.63 % with a FAR of 0.14 %, an FRR of 2.86 %, and a TPR of 97.14 %. Thus, the study supports using ECG biometrics for verification in real-world settings by employing a smart textile shirt with varying temporal variability.

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Section: Methodsmentioning

confidence: 99%

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2024

ARASET

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“…Abundant channels can provide more spatial information and may gain significant performance improvement. For braincomputer-interfaces applications, normally 256 channels [41] or up to 512 channels can be recorded. However, for the longtime sleep monitoring, with the guideline of AASM manual and without disturbing the natural sleep process, thereby few channels (normally less than 8 monopolar EEG channels) were arranged.…”

Section: B Selection Of Channelsmentioning

confidence: 99%

MS-HNN: Multi-Scale Hierarchical Neural Network With Squeeze and Excitation Block for Neonatal Sleep Staging Using a Single-Channel EEG

Zhu

Shen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Most existing neonatal sleep staging approaches applied multiple EEG channels to obtain good performance. However, it potentially increased the computational complexity and led to an increased risk of skin disruption to neonates during data acquisition. In this paper, a multi-scale hierarchical neural network (MS-HNN) with a squeeze and excitation (SE) block for neonatal sleep staging is presented in this study on the basis of a single EEG channel. MS-HNN composes of multi-scale convolutional neural network (MSCNN), temporal information learning (TIL) module, and squeeze and excitation (SE) block. MSCNN can extract features from different scales and frequencies, and TIL module can acquire the transition information among adjacent stages. In addition, for these extracted features, SE block can selectively concentrate on informative features and weaken redundant features for achieving better performance. The proposed approach was validated on a clinical dataset involving 64 neonates from the Children's Hospital of Fudan University (CHFU). The proposed network achieves an accuracy of 75.4% and 76.5% for three-class automatic neonatal sleep staging with the single-EEG channel and the eight-EEG channels, respectively. The experimental results show that the proposed method can maintain good performance by making full use of the information in the single channel while reducing the channels to control the computational overhead.

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“…Figure 1 is a block diagram illustrating the approach framework selected and reconstructed from researches in Toulni et al, [28], Althabhawee et al, [29], Ashwini and Nagaraj [30], Uwaechia and Ramli [31], and Ugi et al, [32]. The framework for identity authentication consists of four phases: data gathering, baseline correction and denoising in pre-processing, fiducials detection in feature extraction, and parameter evaluation in the classification block stage.…”

Section: Proposed Biometric Frameworkmentioning

confidence: 99%

Biometric Analysis on Smart Textile Garment in Real Life Scenario

Muhammad Muizz Mohd Nawawi,

Khairul Azami Sidek,

Amelia Wong Azman

2023

ARASET

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The rapid proliferation of wearable applications and technologies capable of acquiring biomedical signals has prompted the incorporation of biomedical signals, such as the electrocardiogram (ECG), for biometric purposes in wearable platforms. Most ECG biometric research utilises medical-grade sensors in clinical settings, which is unrealistic for wearable ECG-based biometric applications in the real world. Therefore, this research aims to examine the ECG biometric on smart textile garments in real life, collected from commercially available wearable Hexoskin Proshirt and HeartIn Fit shirts. ECG data were obtained from 22 participants who took part in this study. The raw ECG signal is initially pre-processed using noise-removal Butterworth filters in the time domain, followed by an effective QRS segmented feature extraction technique. Finally, around 2076 datasets were created for training and validation, while the remaining 501 datasets were employed to test the suggested recognition approach with 29 Machine Learning Classifiers. Subsequently, Quadratic SVM has the highest accuracy at 96.8% for ECG biometrics, followed by Narrow Neural Network with 95.8% and Wide Neural Network with 95.4%. Further improvement to the QSVM parameter improved the accuracy to 97.4% with an error rate of 2.6%, followed by a sensitivity of 97.4% with a precision of 97.7% and a false rejection rate of 2.6%. Thus, the results of this study further validate the feasibility of applying ECG biometrics for recognition in real-life scenarios utilising a smart textile shirt with different configurations and brand is possible.

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Classification of EEG signal using EACA based approach at SSVEP-BCI

Cited by 5 publications

References 23 publications

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

ECG in Real World Scenario: Time Variability in Biometric Using Wearable Smart Textile Shirts

MS-HNN: Multi-Scale Hierarchical Neural Network With Squeeze and Excitation Block for Neonatal Sleep Staging Using a Single-Channel EEG

Biometric Analysis on Smart Textile Garment in Real Life Scenario

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