Multi-kernel-based random vector functional link network with decomposed features for epileptic EEG signal classification. - Sebamai Parija; Pradipta Kishore Dash; Ranjeeta Bisoi (IET Signal Process., 2020)

Deep long short term memory based minimum variance kernel random vector functional link network for epileptic EEG signal classification. - Sebamai Parija; Ranjeeta Bisoi; P. K. Dash; Mrutyunjaya Sahani (Eng. Appl. Artif. Intell., 2021)

A systematic review on detection and estimation algorithms of EEG signal for epilepsy. - Shazia Hasan; Ameya K. Kulkarni; Sebamai Parija; Pradipta Kishore Dash (Int. J. Medical Eng. Informatics, 2021)

Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification. - Sebamai Parija; Mrutyunjaya Sahani; Ranjeeta Bisoi; P. K. Dash (Pattern Anal. Appl., 2023)

Epileptic EEG signal classification using an improved VMD-based convolutional stacked autoencoder. - Sebamai Parija; Pradipta Kishore Dash; Ranjeeta Bisoi (Pattern Anal. Appl., 2024)