Novel eye‐blink artefact detection algorithm from raw EEG signals using FCN‐based semantic segmentation method

Tosun, Mustafa; Kasım, Ömer

doi:10.1049/iet-spr.2019.0602

Cited by 13 publications

(5 citation statements)

References 29 publications

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“…The simplest of them just reject those fragments of EEG signals with artifacts. Unfortunately, this approach results in the loss of all information from the rejected signal fragments (Hasasneh et al, 2018;Khatwani et al, 2018;Nejedly et al, 2019;Tosun and Kasım, 2020;Iaquinta et al, 2021;Placidi et al, 2021). In addition, we must have a very good artifact detection algorithm that will allow us to identify them.…”

Section: State Of the Artmentioning

confidence: 99%

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

2022

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Electroencephalography (EEG) signals are disrupted by technical and physiological artifacts. One of the most common artifacts is the natural activity that results from the movement of the eyes and the blinking of the subject. Eye blink artifacts (EB) spread across the entire head surface and make EEG signal analysis difficult. Methods for the elimination of electrooculography (EOG) artifacts, such as independent component analysis (ICA) and regression, are known. The aim of this article was to implement the convolutional neural network (CNN) to eliminate eye blink artifacts. To train the CNN, a method for augmenting EEG signals was proposed. The results obtained from the CNN were compared with the results of the ICA and regression methods for the generated and real EEG signals. The results obtained indicate a much better performance of the CNN in the task of removing eye-blink artifacts, in particular for the electrodes located in the central part of the head.

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Section: State Of the Artmentioning

confidence: 99%

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

2022

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“…According to Refs. [39][40][41][42], the attention mechanism plays an important role in segmentation tasks. The decoder attempts to reduce the channel number while increasing the spatial resolution for dense prediction using detailed features provided by skip connections.…”

Section: Feature Attention Selectionmentioning

confidence: 99%

FFUNet: A novel feature fusion makes strong decoder for medical image segmentation

Xie

Zhu

et al. 2022

IET Signal Processing

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Convolutional neural networks (CNNs) have strong ability to extract local features, but it is slightly lacking in extracting global contexts. In contrast, transformers are good at long‐distance modelling due to the global self‐attention mechanisms while its performance in localization is limited. On the other hand, the feature gap between an encoder and decoder is also challenging for a U‐shaped network, which adopts a plain skip connection. Inherited from convolutional networks and transformers, FFUNet, a hybrid network structure with a novel module named the Feature Fusion Module (FFM) is proposed for medical image segmentation. The proposed FFM consisting of Feature Attention Selection, Cross Offset Generation and Deformable Convolution Layer, aims to replace the original plain skip connection to alleviate the ambiguous semantic information between the encoder and decoder for a more powerful medical image segmentation network. Extensive experiments demonstrate that the proposed FFUNet has amazing performance in segmentation gains on the Synapse dataset. In addition, consistent improvements are also achieved across other four popular datasets and CNN‐based or transformer‐based segmentation networks, which illustrate that the proposed method has advantages in generalization and compactness.

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“…So, there is a requirement for developing new methods for improving accuracy. To detect the eye blink objects in EEG signals, the semantic segmentation methods are used and the classification accuracy achieved was found to be 94.4% [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Vishesh¹,

Raghavendra²,

Jankatti³

et al. 2021

IJEECS

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<span><span>Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the “Xiaoyang Tan” of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety</span>.</span>

show abstract

Novel eye‐blink artefact detection algorithm from raw EEG signals using FCN‐based semantic segmentation method

Cited by 13 publications

References 29 publications

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

FFUNet: A novel feature fusion makes strong decoder for medical image segmentation

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

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