EEG Classification for Neurological Disorders Using Frequency Band Deciles

Electroencephalography (EEG) is a widely used non-invasive technique for monitoring brain activity, offering valuable insights into neurological disorders. Feature extraction methods based on signal processing approaches have been shown to be effective, but they tend to overlook the statistical properties of EEG signals. This study proposes a decile-based feature extraction method for EEG signal analysis, aimed at improving classification performance while maintaining simplicity and interpretability. The method was evaluated across multiple tasks, including the classification of Alzheimer’s disease (AD), frontotemporal dementia (FTD), Parkinson’s disease (PD), and seizure detection, using three machine learning models: Random Forest (RF), K-Nearest Neighbors (KNN), and LightGBM. Experimental results demonstrate that the decile-based approach, particularly when paired with RF and KNN, achieves high classification accuracy. Furthermore, the proposed method showed strong robustness to reduced channel counts, highlighting its potential for application in low-cost, wearable EEG systems. While model performance varied across datasets, particularly for LightGBM, the overall results confirm the effectiveness and generalizability of decile-based features in diverse EEG classification tasks. These findings support the method’s potential for clinical use in early diagnosis and real-time monitoring of neurological conditions, especially in resource-constrained or ambulatory settings.