Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sivasakthivel, Ramkumar; Rajagopal, Manikandan; Ramar, Gobinath; Stephen, R.; Sindhu, V.; Fernandiz, Elvis Vidit Title A name given to the resource Analysing Enhanced EEG Based Brain Computer Interface for Motor Imagery Tasks Using Statistical Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Computing Technologies, ICOCT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICOCT64433.2025.11118933" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICOCT64433.2025.11118933</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016315965?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016315965?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sivasakthivel R., Christ University, Karnataka, Bangalore, India; Rajagopal M., Christ University, Karnataka, Bangalore, India; Ramar G., Christ University, Karnataka, Bangalore, India; Stephen R., Christ University, Karnataka, Bangalore, India; Sindhu V., Christ University, Karnataka, Bangalore, India; Fernandiz E.V., Christ University, Karnataka, Bangalore, India Description An account of the resource BCIs have emerged as a useful tool for helping people with neurological disorders like epilepsy, ALS, and cerebral palsy, who have severely limited communication, by analyzing EEG signals and turning them into actionable signals. This paper aims to investigate the application of BCIs in analyzing EEG signals and designing them to give meaningful signals to the users. Five healthy, right-handed university students (18-21 years) were selected for the study and were asked to spell the words mentally without vocalizing for four cognitive tasks; forward, stop, left, and right. At 100 Hz, EEG signals were sampled and pre-processed with a notch filter and feature extraction was done using DWT to extract important features and KNN was used for classification. All the subjects showed high accuracy with more than 90% and maximum accuracy of 95.89% was obtained by Subject S3. Standard deviations between 1.32 to 1.41, which indicate low variability in performance among all the subjects. These results showed that the DWT with KNN combination can be used for real-time BCI applications and can offer a reliable communication method for people with motor impairment and disabilities. 2025 IEEE. Subject The topic of the resource Brain-Computer Interface; Discrete Wavelet Transform; Electroencephalogram; k Nearest neighbour Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151637-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online