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 Article Faculty Publications -Articles 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 Kanna, R. Kishore; Shoran, Preety; Yadav, Meenakshi; Ahmed, Mohammad Nadeem; Burje, Shrikant; Shukla, Garima; Sinha, Anurag; Hussain, Mohammad Rashid; Khalid, Saifullah Title A name given to the resource Improving EEG based brain computer interface emotion detection with EKO ALSTM model 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 Scientific Reports;Volume;15;Issue;1;Article No.;20727; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41598-025-07438-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-025-07438-z</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009607550?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009607550?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 Kanna R.K., Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R &amp; D Institute of Science and Technology, Chennai, India; Shoran P., Department of Computer Science, CHRIST University, Bengaluru, India; Yadav M., Department of Information &amp; Technology, Galgotias College of Engineering and Technology, Greater Noida, India; Ahmed M.N., Department of Computer Science, College of Computer Science, King Khalid University, 61421, Abha, Saudi Arabia; Burje S., Department of Electronics and Telecommunication Engineering, RSR Rungta College of Engineering and Technology, Bhilai, India; Shukla G., Jamia Millia Islamia, New Delhi, India; Sinha A., Tech School, Computer Science Department, ICFAI University, Jharkhand, Ranchi, India; Hussain M.R., Department of Business Informatics, College of Business, King Khalid University, Abha, 62217, Saudi Arabia; Khalid S., IBM Multi Activities Co. Ltd., Khartoum, Sudan Description An account of the resource Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based braincomputer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies. The Author(s) 2025. Subject The topic of the resource Braincomputer interface (BCI); Discrete wavelet transform (DWT); Electroencephalogram (EEG); Emotion detection; Enhanced kookaburra optimized adjustable long short term memory (EKO-ALSTM) Publisher An entity responsible for making the resource available Nature Research Relation A related resource ISSN: 20452322; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online