Significance of Subject-Dependency for Imagined Speech Classification Using EEG

Various diseases like brain-related strokes, aphasia and amyotrophic lateral sclerosis (ALS) lead to speech impair-ments. Speech impairments will negatively affect all aspects of the life of an individual and reduce its quality. Speech BCI can provide an alternative communication interface for both healthy and speech-impaired individuals. Imagined speech can be used to develop intuitive Brain Computer Interfaces (BCIs) for subjects with speech impairments. This study focuses on the subject dependency of EEG signals in the context of imagined vowel phoneme classification. Various feature engineering techniques derived from wavelets, statistical analysis, relative wave energy and Hilbert-Huang transforms were studied under both multi-class and binary classifications to compare the results under subject-independent and subject-dependent classifications. Each classification was done using 23 conventional machine learning models with 10-fold cross-validation. The classifications obtained notable accuracies above the chance level for both multi-class and binary classifications. In every scenario, subject-dependent classification had higher accuracies compared to the subject-independent classification. Additionally, the statistical significance of the mean of the results obtained under subject-dependent classification over subject-independent classification was proven under Welch's t-test. Hence, this study provides solid validation that there is a high subject-dependent nature of EEG signals for the task of phoneme imagination.