Work

Towards Effective Deep Neural Network Approach for Multi-Trial P300-Based Character Recognition in Brain-Computer Interfaces

Year: 2024

Type: conference-paper

Abstract: Brain-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$\mathbf{P} 300$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$\mathbf{6 9. 7 \%}$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.

Source: Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

DOI: 10.1109/smc54092.2024.11169725

Authors: Praveen Kumar Shukla, Hubert Cecotti, Yogesh Kumar Meena

Institutions: Indian Institute of Technology Gandhinagar, California State University, Fresno

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funder: Indian Institute of Technology Gandhinagar

Award: G2679990184