Eye blink artifact detection based on multi-dimensional EEG feature fusion and optimization

Wang, Meng; Cui, Xiaonan; Wang, Tianlei; Jiang, Tiejia; Gao, Feng; Cao, Jiuwen

doi:10.1016/j.bspc.2023.104657

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…This work provides insights for our future endeavors, where we will design regression methods using adaptive approaches. Equally important, inspired by the work of Wang [34] and colleagues, our future research will continue to refine the feature fusion module to enable the more precise extraction of object boundary information, especially for small objects.…”

Section: Discussionmentioning

confidence: 99%

Research on Forest Flame Detection Algorithm Based on a Lightweight Neural Network

Chen,

Wang,

Lin

2023

Forests

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To solve the problem of the poor performance of a flame detection algorithm in a complex forest background, such as poor detection performance, insensitivity to small targets, and excessive computational load, there is an urgent need for a lightweight, high-accuracy, real-time detection system. This paper introduces a lightweight object-detection algorithm called GS-YOLOv5s, which is based on the YOLOv5s baseline model and incorporates a multi-scale feature fusion knowledge distillation architecture. Firstly, the ghost shuffle convolution bottleneck is applied to obtain richer gradient information through branching. Secondly, the WIoU loss function is used to address the issues of GIoU related to model optimization, slow convergence, and inaccurate regression. Finally, a knowledge distillation algorithm based on feature fusion is employed to further improve its accuracy. Experimental results based on the dataset show that compared to the YOLOv5s baseline model, the proposed algorithm reduces the number of parameters and floating-point operations by approximately 26% and 36%, respectively. Moreover, it achieved a 3.1% improvement in mAP0.5 compared to YOLOv5s. The experiments demonstrate that GS-YOLOv5s, based on multi-scale feature fusion, not only enhances detection accuracy but also meets the requirements of lightweight and real-time detection in forest fire detection, commendably improving the practicality of flame-detection algorithms.

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Section: Discussionmentioning

confidence: 99%

Research on Forest Flame Detection Algorithm Based on a Lightweight Neural Network

Chen,

Wang,

Lin

2023

Forests

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“…Furthermore, separating noise becomes challenging when there is spectral overlap between brain signals and interferences. To address these challenges, more sophisticated algorithms like blind source separation (BSS), adaptive filtering, wavelet decompositions (WT), and empirical mode decomposition (EMD) have been proposed to efficiently eliminate interferences from EEG signals [11], [12], [13], [14], [15], [16], [17]. One popular BSS technique, independent component analysis (ICA), effectively separates multichannel EEG signals into independent components, comprising both sources and artifacts [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Real-time Single-Channel EOG removal based on Empirical Mode Decomposition

Nguyen Trong,

Nguyen Luong,

Tan

et al. 2024

EAI Endorsed Trans Ind Net Intel Syst

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In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of essential signal features. Consequently, artifact removal from physiological signals is a crucial step in signal processing pipelines. Current techniques often employ Independent Component Analysis (ICA) to efficiently separate signal and artifact sources in multichannel recordings. However, limitations arise when dealing with single or a few channel measurements in minimal instrumentation or portable devices, restricting the utility of ICA. To address this challenge, this paper introduces an innovative artifact removal algorithm utilizing enhanced empirical mode decomposition to extract the intrinsic mode functions (IMFs). Subsequently, the algorithm targets the removal of segments related to EOG by isolating them within these IMFs. The proposed method is compared with existing single-channel EEG artifact removal algorithms, demonstrating superior performance. The findings demonstrate the effectiveness of our approach in isolating artifact components, resulting in a reconstructed signal characterized by a strong correlation and a power spectrum closely resembling the ground-truth EEG signal. This outperforms the existing methods in terms of artifact removal. Additionally, the proposed algorithm exhibits significantly reduced execution time, enabling real-time online analysis.

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“…The first motivation involves the elimination and/or adaptation of electroencephalographic (EEG) signals, since the presence of artifacts renders such signals unusable for drawing conclusions (e.g., diagnoses). In the former case, the data set that needs to be processed is reduced, and in the latter case, the quality of the data set to be processed is improved [1][2][3]. These studies typically include healthy individuals as well as those with some kind of pathology.…”

Section: Introductionmentioning

confidence: 99%

“…Blink detection implies that this peak is narrow and pronounced, so it has a high value; Detection involves choosing a threshold for the above features that determines the presence of a blink. These methods are the simplest and fastest [3]. The main problem with these methods is the correct choice of the threshold used.…”

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confidence: 99%

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A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset

López-Ahumada

Jiménez-Naharro

Gómez-Bravo

2023

Sensors

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Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human–computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain–computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software.

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Eye blink artifact detection based on multi-dimensional EEG feature fusion and optimization

Cited by 8 publications

References 31 publications

Research on Forest Flame Detection Algorithm Based on a Lightweight Neural Network

Research on Forest Flame Detection Algorithm Based on a Lightweight Neural Network

Real-time Single-Channel EOG removal based on Empirical Mode Decomposition

A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset

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