Mesin, LGhani, UNiazi, IK2023-06-132023-06-132023-03-05Electronics (Switzerland), ISSN: 2079-9292 (Print); 2079-9292 (Online), MDPI AG, 12(5), 1246-1246. doi: 10.3390/electronics120512462079-92922079-9292https://hdl.handle.net/10292/16263The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications.https://creativecommons.org/licenses/by/4.0/40 Engineering4003 Biomedical EngineeringNeurological0906 Electrical and Electronic Engineering4009 Electronics, sensors and digital hardwareJournal ArticleOpenAccess10.3390/electronics12051246