Decoding Attention through EEG: Paving the Way for BCI Applications in Attention-Related Disorders.

This study investigates attention-related traits in EEG signals to assess the potential of Electroencephalography (EEG) as an objective diagnostic tool for attention-related disorders such as ADHD, anxiety, and learning disabilities. EEG data were collected from 31 participants, including individuals with ADHD, while they performed a Go/No-Go task designed to evaluate attention and impulsivity. The analysis focused on the spectral characteristics of brain activity, examining the relative power of theta, alpha, and beta frequency bands, along with the theta-to-beta ratio (TBR), to identify distinguishing patterns of attention-related brain activity. Results indicate that the ADHD group exhibited higher theta power and consistently elevated TBR, particularly in the Frontal, Temporal, and Occipital brain regions. Machine learning models, such as K-Nearest Neighbors, effectively classified ADHD and Control groups based on TBR with high accuracy. Additionally, the ADHD group demonstrated faster reaction times but made more errors on the Go/No-Go task, highlighting difficulties with sustained attention. These findings suggest that this approach holds promise for developing objective diagnostic tools for attention-related disorders. While some limitations exist, this study underscores the potential of integrating EEG with machine learning to create brain-computer interface (BCI) systems for assessing attention processes.