Unveiling the influence of facial expressions on EEG-based biometric system performance in ADHD and healthy children
Maryam Safardoost1, Zahra Tabanfar1, Farnaz Ghassemi1
1Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Insights
Electroencephalography (EEG) brain connectivity reliably identifies children, even with emotional changes. This robust biometric system works for both typically developing children and those with ADHD.
Area of Science:
- Neuroscience
- Biometrics
- Developmental Psychology
Background:
- Reliable child identification is crucial for safety and healthcare.
- Electroencephalography (EEG) offers a forgery-resistant biometric marker.
- Emotional states can impact physiological signals, posing challenges for biometrics.
Purpose of the Study:
- To assess the effectiveness of EEG-based biometrics in identifying children across different emotional states.
- To investigate the performance of EEG biometrics in typically developing (TD) children versus children with Attention-Deficit/Hyperactivity Disorder (ADHD).
- To evaluate the robustness of brain connectivity features for emotion-insensitive child identification.
Main Methods:
- EEG data collected from 25 TD children and 22 children with ADHD exposed to happy, sad, angry, and neutral facial expressions.
- Brain connectivity features extracted using directed connectivity metrics (e.g., Directional Transfer Function - DTF).
- Clustering analysis using Riemannian and Euclidean distances to evaluate identification accuracy.
Main Results:
- The Directional Transfer Function (DTF) combined with Riemannian distance-based clustering achieved near-perfect identification accuracies (up to 100%) across all emotional states for both groups.
- Specific accuracies ranged from 99% to 100% for TD children and 99.77% to 100% for children with ADHD.
- No significant differences in identification accuracy were found between emotional states or between the TD and ADHD groups.
Conclusions:
- EEG-based biometrics using brain connectivity are highly accurate and robust to emotional variations in children.
- The developed system demonstrates feasibility for emotion-insensitive child identification, including neurodevelopmental populations like those with ADHD.
- Brain connectivity analysis is a valuable tool for enhancing biometric performance in diverse pediatric populations.
Abstract:
The growing need for reliable biometric systems to identify children in contexts such as vaccination tracking, missing child recovery, and hospital safety has highlighted the importance of using robust physiological markers. EEG signals have emerged as promising biometric indicators due to their resistance to external manipulation and forgery. In this study, we investigated the influence of emotional facial expressions on EEG-based biometric identification in children with and without Attention-Deficit/Hyperactivity Disorder (ADHD). EEG data were recorded from 25 typically developing (TD) children and 22 children diagnosed with ADHD during exposure to four emotional facial expressions: happy, sad, angry, and neutral. To evaluate the robustness of biometric identification across emotional states, brain connectivity features were extracted using various directed connectivity metrics (Directional Transfer Function (DTF), ffDTF, dDTF, dDTF08, Partial directional coherence (PDC)) and analyzed through clustering techniques based on Riemannian and Euclidean distances. The DTF feature combined with Riemannian distance-based clustering achieved the highest identification accuracies across all emotional states, reaching up to 100% for both groups. Specifically, accuracies of 99%, 99.4%, 99.6%, and 100% for healthy children and 100%, 99.77%, 99.77%, and 100% for ADHD children were obtained for sad, happy, angry, and neutral emotions, respectively. Statistical analysis confirmed the emotional resilience of the biometric system, showing no significant differences in identification accuracy between emotional states or between ADHD and TD groups. These findings support the feasibility of emotion-insensitive EEG-based biometric systems for child identification and highlight the utility of brain connectivity features in enhancing performance across neurodevelopmental populations.
