Towards Effective Deep Neural Network Approach for Multi-Trial P300-based Character Recognition in Brain-Computer Interfaces
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913541851709440 |
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| author | Shukla, Praveen Kumar Cecotti, Hubert Meena, Yogesh Kumar |
| author_facet | Shukla, Praveen Kumar Cecotti, Hubert Meena, Yogesh Kumar |
| contents | 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 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 spatio-sequential 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-the-art models in the five-repetition and delivered comparable performance in the ten and fifteen repetitions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08561 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Effective Deep Neural Network Approach for Multi-Trial P300-based Character Recognition in Brain-Computer Interfaces Shukla, Praveen Kumar Cecotti, Hubert Meena, Yogesh Kumar Human-Computer Interaction 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 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 spatio-sequential 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-the-art models in the five-repetition and delivered comparable performance in the ten and fifteen repetitions. |
| title | Towards Effective Deep Neural Network Approach for Multi-Trial P300-based Character Recognition in Brain-Computer Interfaces |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.08561 |