EEG dataset of children with learning disabilities (LD)

When using this resource, kindly cite the original publication: Sase, T., & Othman, M. (2022). Prediction of ADHD from a Small Dataset Using an Adaptive EEG Theta/Beta Ratio and PCA Feature Extraction. In International Conference on Soft Computing and Data Mining (pp. 101-110). Springer, Cham. Participants Participants are 4 children with learning disabilities (LD) (boys and girls, aged 5-8 years). The LD children were diagnosed by health professionals, and none of -them were under medications. All of the children are right-handed. Hardware and dataset EEG recordings were performed using 19-channels Brainmarker EEG machine with the sampling rate of 250Hz and Pz as the reference electrode. The presented dataset is for the following tasks: 1) eyes closed (1 minute), and 2) watching facial expressions for the emotion fear from the Radboud Faces Database (RafD). The emotionally related facial expressions may help the children to change his/her feelings. The experimental protocol was approved by the IIUM Research Ethics Committee No 419 (IREC 419). The file names have the following format: {participant_id}_{task}.csv