Spectral EEG Dataset for Motor Intention Classification and Cognitive State Analysis

This dataset contains spectral band-power features derived from continuous electroencephalography (EEG) recordings acquired with an Emotiv headset during two Brain–Computer Interface (BCI) paradigms, Pull/Push and Left/Right, provided as comma-separated values (CSV) files. It was developed to support the development, validation, and benchmarking of machine learning and deep learning algorithms for motor intention classification, cognitive state recognition, and adaptive BCI applications. The spectral features were extracted without prior aggressive artifact removal, retaining the frequency-domain representation of physiological (e.g., eye blinks, eye movements, and muscle activity) and non-physiological (e.g., head movement and electrode contact variations) artifacts. This enables the evaluation of robust feature engineering and classification methods under realistic acquisition conditions. In addition to motor command decoding, the dataset can be reused to investigate attention, cognitive workload, mental fatigue, and indirect stress-related responses during BCI interaction. By providing standardized spectral EEG features collected under controlled experimental protocols, this dataset promotes reproducible research and serves as a valuable benchmark for neuroengineering, biomedical signal processing, cognitive neuroscience, explainable artificial intelligence, and human–computer interaction studies.