CSP-based discriminative capacity index from EEG supporting ADHD diagnosis

The Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. Although biomarkers as TBR and P300 rely on ADHD physiology, differences between ADHD and control lack significance. In this work, we propose a feature extraction approach based on the common spatial patterns (CSP) from EEG signals to support the ADHD diagnosis. Our features quantify the channel-wise discriminative capacity from the resulting spatial patterns and eigenvalues. We validated the proposed methodology using synthetic and real EEG signal. In the former, the proposed index suitably identifies the spatial location of differentiating sources, while attenuates the common activity. In the latter, the resulting subject-wise features fed a linear discriminant analysis as the supported-diagnosis tool. Achieved 87% accuracy rate proves that the discriminative index identifies outperforms conventional biomarkers in the ADHD diagnosis.

Automated summary

ADHD is a childhood-onset neurological disorder that affects attention, memory, and productivity. A feature extraction approach based on EEG signals was proposed to support ADHD diagnosis, showing superior diagnostic capability compared to conventional biomarkers in experimental validation.