A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG

Motor imagery brain-computer interfaces (MI-BCIs) based on electroencephalography (EEG), a promising technology to provide assistance and support rehabilitation of neurological patients with sensorimotor impairments, require a reliable and adaptable subject-specific model to efficiently decode motor intention. The most popular EEG feature extraction algorithm for MI-BCIs is the common spatial patterns (CSP) method, but its performance strongly depends on the predefined frequency band and time segment length for analyzing the EEG signal.

In this work, a novel method for efficiently decoding motor intention for EEG-based BCIs performing multiple frequency band analysis in multiple EEG segments is presented. This decoding algorithm uses raw multichannel EEG data which are decomposed into specific [Formula: see text] temporal and [Formula: see text] frequency bands. Features are extracted at each [Formula: see text]-[Formula: see text] band by using CSP. Feature selection and classification are simultaneously performed by means of a fast procedure, based on elastic-net regression, which allows for the inclusion of a priori discriminative information into the model. The effectiveness of the proposed method is tested off-line on two public EEG-based MI-BCI datasets and on a self-acquired dataset in two configurations: multiple temporal windows and single temporal window.

The experimental results show that the proposed multiple time-frequency band method yields overall accuracy improvements of up to [Formula: see text] (average accuracy of 84.8%) as compared to the best current state-of-the-art methods based on filter bank analysis and CSP for MI detection. Also, classification variability is reduced, making the proposed method more robust to intra-subject EEG fluctuations.

This paper presents a novel approach for improving motor intention detection by automatically selecting subject-specific spatio-temporal-spectral features, especially when MI has to be detected against rest condition. This technique contributes to the further advancement and application of EEG-based MI-BCIs for assistance and neurorehabilitation therapy.