  Locked-in Syndrome patients are often misdiagnosed and face pessimistic
prognosis because of similarities with disorders of consciousness, a lack of
objective biomarkers and a difficult-to-recognize pathogenesis. Biomarkers show
promise in identifying similar conditions, utilizing electroencephalography
(EEG) data. This data, particularly in the form of event-related potentials
(ERPs), while successful in varying applications, suffers from methodological
constraints and interpretation obstacles. The study documented in this body of
work explores a machine learning paradigm with regards to N400 ERP data
retrieved from a sentence comprehension task to tackle these hindrances and
proposes a new auxiliary diagnostic tool for LIS and possibly disorders of
consciousness. A support vector machine (SVC) and a random forest classifier
(RF) were able to classify conscious individuals from unconscious ones with
optimistic performance metrics. Based on these results, the proposed models and
continuations thereof present valuable opportunities for the development of an
auxiliary diagnostic tool for the classification of LIS patients, aiding
diagnosis, improving prognosis, stimulating recovery and reducing mortality
rates.
