Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis

Farha, Nadia Abu; Al-Shargie, Fares; Tariq, Usman; Al‐Nashash, Hasan

doi:10.3390/s22083051

Cited by 8 publications

(3 citation statements)

References 67 publications

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“…Challenges associated with extracting the gamma band from EEG signals include the presence of artifacts, low signal-to-noise ratio and individual variability. EEG signals are susceptible to various artifacts such as eye blinks, muscle movements and electrical interference [21]. These artifacts can contaminate the gamma band activity and affect the accuracy of extraction techniques.…”

Section: Advantages and Limitations Of Extraction Techniquesmentioning

confidence: 99%

Analysing Gamma Frequency Components in EEG Signals: A Comprehensive Extraction Approach

Hasib,

Vengadasalam

2023

JIWE

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Gamma band activity is a high-frequency (30-100 Hz) oscillation of the electroencephalogram (EEG) that has been linked to a variety of cognitive processes including attention, memory and learning. However, extracting gamma band activity from EEG data can be challenging due to the relatively low signal-to-noise ratio of gamma band signals and the presence of other frequency bands such as beta and alpha. In this paper, we present a method for extracting gamma band activity from EEG data. We evaluated our method on a dataset of EEG data recorded from dyslexic patients. We found that our method was able to successfully extract gamma band activity from the EEG data. The extracted gamma band activity was significantly correlated with the subjects' performance on the visual attention task. Our results suggest that our method is an easy and straightforward approach for extracting gamma band activity from EEG data. This could be used to study the neural basis of cognitive processes in a variety of research settings.

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Section: Advantages and Limitations Of Extraction Techniquesmentioning

confidence: 99%

Analysing Gamma Frequency Components in EEG Signals: A Comprehensive Extraction Approach

Hasib,

Vengadasalam

2023

JIWE

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“…The power spectral density (PSD) feature was used to analyze the subject's EEG signals. Fast Fourier Transformation (FFT) was employed to perform the EEG data analysis to extract the PSD [38].…”

Section: Feature Extraction a Eeg Data Feature Extractiomentioning

confidence: 99%

Brain Region-Based Vigilance Assessment Using Electroencephalography and Eye Tracking Data Fusion

et al. 2022

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Vigilance is the capacity to remain alert for an extended time while performing a task. Staying alert is obligatory in many jobs, particularly those that involve monitoring, such as surveillance tasks, security monitoring, and air traffic control. These monitoring tasks require a specific level of arousal to maintain an adequate level of cognitive efficiency. In this study, we investigate the possibility of assessing the vigilance levels using a fusion of electroencephalography (EEG) and eye tracking data. Vigilance levels were established by performing a modified version of the Stroop color word task (SCWT) for 30 minutes. Feature-level fusion based on the canonical correlation analysis (CCA) was employed to each brain region to improve the classification accuracy of vigilance level assessment. Results obtained using support vector machines (SVM) classifier show that fusion of EEG+eye tracking modalities has improved the classification accuracy compared to individual modality. The EEG+Eye tracking fusion on the right central brain region achieved the highest classification accuracy of 97.4 ± 1.3%, compared to the individual Beta EEG with 92.0± 7.3% and Eye tracking with 76.8± 8.4%, respectively. Likewise, EEG and Eye tracking fusion on the right frontal region showed classification accuracy of 96.9 ± 1.1% for both the Alpha and Beta bands. Meanwhile, when all brain regions were utilized, the highest classification accuracy of EEG+Eye tracking was 96.8 ± 0.6% using Delta band compared to the EEG alone with 88.18 ± 8.5% and eye tracking alone with 76.8 ± 8.4 %, respectively. The overall results showed that vigilance is a brain region specific and the fusion of EEG+ and Eye tracking data using CCA has significantly improved the classification accuracy of vigilance levels assessment.

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“…Zheng et al (2022) combined integrated empirical mode decomposition with power spectral density (PSD) to explore new EEG features for driving fatigue detection. Abu Farha et al (2022) proposed a new wavelet independent component analysis for processing EEG signals to reduce the impact of artifacts. This method outperforms existing independent component analysis (ICA) methods in feature extraction.…”

Section: Introductionmentioning

confidence: 99%

3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

Peng

Gao

Wang

et al. 2023

JDSIS

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Fatigue driving has become one of the main causes of traffic accidents, and driving fatigue detection based on electroencephalogram (EEG) can effectively evaluate the driver’s mental state and avoid the occurrence of traffic accidents. This article evaluates a feature extraction method for extracting multiple features of EEG signals and establishes a Spatiotemporal Convolutional Neural Network (STCNN) to detect driver fatigue. Firstly, we constructed a three-dimensional feature of the EEG signal, which includes the frequency domain, time domain, and spatial features of the EEG signal. Then, we use STCNN for fatigue state classification. STCNN is composed of an Attention Time Network (ATNet) based on attention mechanism and an Attention Convolutional Neural Network (ACNN) based on attention mechanism. In addition, we conducted fatigue driving experiments and collected EEG signals from 14 subjects in both awake and fatigued states, ultimately collecting EEG data under three different driving task loads. We conducted extensive experiments on this basis and compared the effectiveness of STCNN and six competitive methods. The results show that the classification accuracy of STCNN is 87.55%, which can effectively detect the fatigue status of drivers.

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Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis

Cited by 8 publications

References 67 publications

Analysing Gamma Frequency Components in EEG Signals: A Comprehensive Extraction Approach

Analysing Gamma Frequency Components in EEG Signals: A Comprehensive Extraction Approach

Brain Region-Based Vigilance Assessment Using Electroencephalography and Eye Tracking Data Fusion

3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

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