3 accepted papers
Electroencephalogram (EEG) signal classification for neural activity monitoring and cognitive state assessment using Machine Learning (ML) models is a well-established strategy. The usage of ML models with traditional Euclidean vector-based feature extraction is efficient and effective if the behavi…
Usage of Machine Learning (ML) models has been trending for automated screening of mental health. Electroencephalogram (EEG) signals, due to their non-invasive nature and ease of availability with low cost, are mostly recorded and used for diagnosis. Such signals however are non-stationary and also…
The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this work, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multi…