Simultaneous Eye Blink Characterization and Elimination From Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection

Shahbakhti, Mohammad; Beiramvand, Matin; Rejer, Izabela; Augustyniak, Piotr; Broniec-Wójcik, Anna; Wierzchoń, Michał; Marozas, Vaidotas

doi:10.1109/jbhi.2021.3096984

Cited by 52 publications

(25 citation statements)

References 60 publications

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“…Since EEG represents the combination of internal neurological sources, ICA is usually applied to identify and remove artifactual components such as blinks. Blink components display high variance and present a spatial distribution toward the frontotemporal region of the head (Shahbakhti et al, 2022 ). Therefore, to identify them, we followed an automated electro-oculogram proxy approach.…”

Section: Methodsmentioning

confidence: 99%

An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography

Pérez-Valero

Morillas

López-Gordo

et al. 2022

Front. Neuroinform.

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Early detection is crucial to control the progression of Alzheimer's disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.

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Section: Methodsmentioning

confidence: 99%

An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography

Pérez-Valero

Morillas

López-Gordo

et al. 2022

Front. Neuroinform.

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“…• • Information transfer rate (ITR) Information Transfer Rate (ITR) [35] is a standard measure of performance used in BCI applications expressed in bits per minute(bpm) It combines the accuracy and the speed of the classifier into a single number which shows how much information is transferred by the BCI in one unit of time, defined as follows:…”

Section: Fig 3: Details Of Confusion Matrixmentioning

confidence: 99%

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Mathur

Chakka

2022

IEEE Sensors J.

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Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multichannel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs.

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“…The central area of the box may be brighter than that of the neighboring boxes, which might be misinterpreted for a nose. Viola and Jones observed three sorts of Haar-like characteristics in their research: • Four-sided features • Edge features • Line features [11] 4) Integral images: The integral picture aids us in performing these time-consuming computations fast enough that we may determine if a feature of a group of features meets the requirements [12].…”

Section: A Viola-jones Modelmentioning

confidence: 99%

“…The frequency and duration are the prime factors for blinking. For an adult, the Average blinking frequency is 15-20 blinks/min, while the blinking frequency of the children is lower [12][13][14][15].…”

Section: Introductionmentioning

confidence: 96%

Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

Gawande,

Badotra

2022

IJACSA

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In this research work, a novel eye-blink detection model is developed. The proposed eye blink detection model is modeled by following seven major phases: (a) video-to-frame conversion, (b) Pre-processing, (c) face detection, (d) eye region localization, (e) eye landmark detection and eye status detection, (f) eye blink detection and (f) Eye blink Classification. Initially, from the collected raw video sequence (input), each individual frames are extracted in the video -to-frame conversion phase. Then, each of the frames is subjected to pre-processing phase, where the quality of the image in the frames is improved using proposed Kernel median filtering (KMF) approach. In the face detection phase, the Viola-Jones Model has been utilized. Then, from the detected faces, the eye region is localization within the proposed eye region localization phase. The proposed Eye region localization phase encapsulates two major phases: Feature extraction and landmark detection. The features like improved active shape models (I-ASMs), Local Binary pattern are extracted from the detected facial images. Then, the eye region is localization by using a new optimized Convolution neural network framework. This optimized CNN framework is trained with the extracted features (I-ASM and LBP). Moreover, to enhance the classification accuracy of eye localization, the weight of CNN is fine-tuned using a new Seagull Optimization with Enhanced Exploration (SOEE), which is the improved version of standard Seagull Optimization Algorithm (SOA). The outcome from optimized CNN framework is providing the exact location of the eye region. Once the eye region is detected, it is essential to detect the status of the eye (whether open or close). The status of the eye is detected by computing the eye aspect ratio (EAR). Then, the identified eye blinks are classified based on the computed correlation coefficient as long and short blinks. Finally, a comparative evaluation has been accomplished to validate the projected model.

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Simultaneous Eye Blink Characterization and Elimination From Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection

Cited by 52 publications

References 60 publications

An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography

An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

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