@InProceedings{Abbas2026_1294,
<br/>   author = {Syed Hur Abbas and Peter Birkholz and Muhammad Arif},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2026, Tagungsband der 37. Konferenz},
<br/>   title = {Feature-Enhanced Consensus Graph Model for EEG-based Imagined Word Recognition},
<br/>   year = {2026},
<br/>   editor = {Günther Wirsching},
<br/>   month = mar,
<br/>   pages = {187--194},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {For people with speech impairments, imagined speech provides a way to communicate by mentally formulating words without engaging vocal organs. The good temporal resolution, portability, safety, and cost-effectiveness of Electroencephalography (EEG) have made it a prominent non-invasive method for studying imagined speech. Recently, a multi-view  graph fusion model was proposed, named as KGMV, which learned a consensus graph from multiple EEG features in a Reproducing Kernel Hilbert Space (RKHS). An accuracy of 81.73% was reported on 5-class imagined word classification using a limited set of temporal, statistical, and frequency-domain views. Much of the potentially informative feature space thus remained unexplored. In this work, we extended KGMV to a Feature Enhanced KGMV (FE-KGMV) framework that (1) augmented the feature pool with additional temporal, statistical, and frequency-domain features and (2) applied a subject-wise view-ranking step based on a fused discriminability score (MV-Fisher, ANOVA F, CV-LDA) to retain only the most informative features before graph learning. On the same dataset, FE-KGMV reached a mean accuracy of 86.27%, corresponding to a 4.54 percentage-point improvement over the baseline KGMV.},
<br/>   isbn = {978-3-95908-834-3},
<br/>   issn = {0940-6832},
<br/>   keywords = {Posters},
<br/>   url = {https://www.essv.de/pdf/2026_187_194.pdf},
<br/>}