Children diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) face many difficulties in maintaining their concentration (in terms of attention levels) and controlling their behaviors. Previous studies have mainly focused on identifying brain regions involved in cognitive processes or classifying ADHD and control subjects. However, the classification of attention levels of ADHD subjects has not yet been explored. Here, a robust Swin Transformer (Swin-T) model is proposed to classify the attention levels of ADHD subjects. The experimental cognitive task ‘Surround suppression’ includes two events: Stim ON and Stim OFF related to the high and low attention levels of a subject. In the proposed framework, ADHD-specific channels are initially identified from input Electroencephalography (EEG). Next, the significant, non-noisy connectivity features are extracted from those channels through the Singular Value Decomposition (SVD) method. Finally, the non-noisy features are passed to the robust Swin-T model for attention-level classification. The proposed model achieves 97.28% classification accuracy with 12 subjects. The robustness of the proposed model leads to potential benefits in EEG-based research and clinical settings, enhancing the reliability of ADHD assessments.