Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based Systems Using Multidomain Electroencephalography

Motor imagery-based brain-computer interfaces (BCIs) have potential applications in neurorehabilitation; however, the high dimensionality and complexity of electroencephalography (EEG) signals present challenges for accurate classification. This study aimed to improve the classification of motor and motor imagery activities using multidomain feature engineering and feature selection on the MILimbEEG dataset. Time-domain, frequency-domain, and wavelet-based features were extracted from 16-channel EEG signals, yielding 416 features. Maximum relevance minimum redundancy (MRMR) was then applied to

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