Shukla, Praveen KumarPraveen KumarShuklaCecotti, HubertHubertCecottiMeena, Yogesh KumarYogesh KumarMeena2026-01-292026-04-022026-01-292024-01-0110.1109/SMC54092.2024.111697252-s2.0-105027636084https://repository.iitgn.ac.in/handle/IITG2025/34043Brain-computer interfaces (BCIs) enable direct interaction between users and computers by decoding brain signals. This study addresses the challenges of detecting P300 event-related potentials in electroencephalograms (EEGs) and integrating these P300 responses for character spelling, particularly within oddball paradigms characterized by uneven P300 distribution, low target probability, and poor signal-to-noise ratio (SNR). This work proposes a weighted ensemble spatiosequential convolutional neural network (WE-SPSQ-CNN) to improve classification accuracy and SNR by mitigating signal variability for character identification. We evaluated the proposed WE-SPSQ-CNN on dataset II from the BCI Competition III, achieving P300 classification accuracies of 69.7% for subject A and 79.9 % for subject B across fifteen epochs. For character recognition, the model achieved average accuracies of 76.5 %, 87.5 %, and 94.5 % with five, ten, and fifteen repetitions, respectively. Our proposed model outperformed state-of-theart models in the five-repetition and delivered comparable performance in the ten and fifteen repetitions.en-USfalseConference Papernull257716551433-143820240cpConference Paper