Artifact Removal of Eye Tracking Data for the Assessment of Cognitive Vigilance Levels

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

doi:10.1109/icabme53305.2021.9604870

Cited by 4 publications

(8 citation statements)

References 33 publications

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“…First, we defined two vigilance states for the subjects within the 30 min EEG recordings: the alert state, including the first 5 min of EEG signals, and the vigilance decrement state, which refers to the last 5 min of EEG signals within the SCWT. The criteria for this decision were based on behavioral data analysis as reported in our previous study [28]. Second, the clean EEG signals were analyzed using the fast Fourier transform method to extract the Power Spectral Density (PSD).…”

Section: Feature Extractionmentioning

confidence: 99%

“…We have employed five classifiers, namely KNN, Discriminant Analysis, Naive Bayes, Decision Tree, and SVM to distinguish between the two vigilance levels. The selected classifiers are widely recognized in the field of brain-computer interfaces for being fast and reliable [28]. KNN is known for training in a fast manner, SVM provides high accuracy, and Naive Bayes is fast and can be used to make real-time predictions [41].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Discriminant Analysis also showed a slightly better classification accuracy in Table 1 compared to Table 2. Table 3 compares the vigilance assessment accuracy based on the power spectral density between our study and [28]. The comparison is between the three EEG preprocessing approaches used for the vigilance assessment: wICA and Fast-ICA were adopted in this study, and traditional ICA was used in [28].…”

Section: Classificationmentioning

confidence: 99%

“…The study achieved a high classification accuracy of 91.4% in detecting spikes using the Daub-20 wavelet function. Recent vigilance and emotion studies [28,29] used ICA with visual in-spection to eliminate noise, eye blinks, and movement artifacts. The studies identified and removed 2-frontal ICs that were highly associated with artifacts and reconstructed the rest of the ICs to form the cleaned EEG signals.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Farha

Al-Shargie

Tariq

et al. 2022

Sensors

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Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color–Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Farha

Al-Shargie

Tariq

et al. 2022

Sensors

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show abstract

“…Basically, the blink duration increases with prolonged task duration, regardless of task level. Study [17] investigated the accuracy of vigilance level assessment obtained from different classifiers by utilizing six eye tracking features, The pupil size feature showed the highest individual classification accuracy among all the six Eye tracking features. From the same study, a higher accuracy of 76.8 ± 8.4% was achieved when all the six features were fed to the SVM classifier.…”

Section: Introductionmentioning

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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Automated Assesment of Eye-hand Coordination Skill using a Vertical Tracing Task on a Gaze-sensitive Human Computer Interaction Platform for children with Autism

Rane,

Singh,

Lahiri

2024

Proc. ACM Hum.-Comput. Interact.

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Children with Autism often demonstrate atypical gaze pattern and eye-hand coordination skill deficits marked by difficulties in reaching out for an object, tracing on a vertically mounted canvas, etc. Currently existing conventional methods can assess one's coordination skill during hand movement in 3D-space. But such methods can be subjective and devoid of gaze tracking. Investigation of coordination skill and gaze tracking of this target group in tasks set in 3D-space has been largely unexplored. To quantitatively assess one's eye-hand coordination skill, we have designed Virtual Reality-based Automated gaze-sensitive Tool that can help understand the linkage between their gaze and 3D performance. Results of a study with 10 pairs of age-matched children with Autism (GroupASD) and typically developing children (GroupTD) showed that GroupASD demonstrated reduced eye-hand coordination skill (increased tracing error in vertical tracing task) accompanied with reduced fixation duration on task-relevant regions and atypical gaze path than GroupTD.

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Artifact Removal of Eye Tracking Data for the Assessment of Cognitive Vigilance Levels

Cited by 4 publications

References 33 publications

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

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

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

Automated Assesment of Eye-hand Coordination Skill using a Vertical Tracing Task on a Gaze-sensitive Human Computer Interaction Platform for children with Autism

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