Yes/No Classification of EEG data from CLIS patients.

The goal of this research is to evaluate the usability of new features to classify EEG data from several completely locked-in patients (CLIS), and eventually build a more reliable communication system for them. Patients in such state are completely paralyzed, preventing them to be able to talk, but they retain their cognitive abilities.The data were obtained from four CLIS patients and recorded during an auditory paradigm task during which they were asked yes/no questions. Spectral measures such as the relative power of δ, θ, α, β and γ frequency bands, spectral edge frequencies (SEF50 and SEF95), complexity measure obtained from Poincaré plots and connectivity measures such as the imaginary part of coherency and the weighted Symbolic Mutual Information (wSMI) were used as features. The data was classified using Random Forest and Support Vector Machine, two methods successfully used to classify mental states in both healthy subjects and patients. Additionally, two cases were studied. The first case uses data recorded when the patient is answering questions, while in the second case it also includes data recorded when the experimenter is asking the questions.The classification accuracy during training varies between 51.73 to 67.72% in the first case, and from 50.41 to 67.94% for the second case. Overall, wSMI with a time lag of 64 ms gave the best classification accuracy and in general, Random Forest appears to be the best classification method.Clinical relevance This case study investigates the usability of new features based on EEG complexity and connectivity to classify CLIS patients brain signal, what results in a further step toward the demand of more effective EEG-based Brain-Computer Interface communication systems for CLIS patients.