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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.15161 |
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| _version_ | 1866908097508802560 |
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| author | Parthasarathy, Nithin Soetedjo, James Panchavati, Saarang Parthasarathy, Nitya Arnold, Corey Pouratian, Nader Speier, William |
| author_facet | Parthasarathy, Nithin Soetedjo, James Panchavati, Saarang Parthasarathy, Nitya Arnold, Corey Pouratian, Nader Speier, William |
| contents | Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, severely restricts patient communication capacity within a few years of onset, resulting in a significant deterioration of quality of life. The P300 speller brain computer interface (BCI) offers an alternative communication medium by leveraging a subject's EEG response to characters traditionally highlighted on a character grid on a graphical user interface (GUI). A recurring theme in P300-based research is enhancing performance to enable faster subject interaction. This study builds on that theme by addressing key limitations, particularly in the training of multi-subject classifiers, and by integrating advanced language models to optimize stimuli presentation and word prediction, thereby improving communication efficiency. Furthermore, various advanced large language models such as Generative Pre-Trained Transformer (GPT2), BERT, and BART, alongside Dijkstra's algorithm, are utilized to optimize stimuli and provide word completion choices based on the spelling history. In addition, a multi-layered smoothing approach is applied to allow for out-of-vocabulary (OOV) words. By conducting extensive simulations based on randomly sampled EEG data from subjects, we show substantial speed improvements in typing passages that include rare and out-of-vocabulary (OOV) words, with the extent of improvement varying depending on the language model utilized. The gains through such character-level interface optimizations are approximately 10%, and GPT2 for multi-word prediction provides gains of around 40%. In particular, some large language models achieve performance levels within 10% of the theoretical performance limits established in this study. In addition, both within and across subjects, training techniques are explored, and speed improvements are shown to hold in both cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15161 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training Parthasarathy, Nithin Soetedjo, James Panchavati, Saarang Parthasarathy, Nitya Arnold, Corey Pouratian, Nader Speier, William Human-Computer Interaction Computation and Language Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, severely restricts patient communication capacity within a few years of onset, resulting in a significant deterioration of quality of life. The P300 speller brain computer interface (BCI) offers an alternative communication medium by leveraging a subject's EEG response to characters traditionally highlighted on a character grid on a graphical user interface (GUI). A recurring theme in P300-based research is enhancing performance to enable faster subject interaction. This study builds on that theme by addressing key limitations, particularly in the training of multi-subject classifiers, and by integrating advanced language models to optimize stimuli presentation and word prediction, thereby improving communication efficiency. Furthermore, various advanced large language models such as Generative Pre-Trained Transformer (GPT2), BERT, and BART, alongside Dijkstra's algorithm, are utilized to optimize stimuli and provide word completion choices based on the spelling history. In addition, a multi-layered smoothing approach is applied to allow for out-of-vocabulary (OOV) words. By conducting extensive simulations based on randomly sampled EEG data from subjects, we show substantial speed improvements in typing passages that include rare and out-of-vocabulary (OOV) words, with the extent of improvement varying depending on the language model utilized. The gains through such character-level interface optimizations are approximately 10%, and GPT2 for multi-word prediction provides gains of around 40%. In particular, some large language models achieve performance levels within 10% of the theoretical performance limits established in this study. In addition, both within and across subjects, training techniques are explored, and speed improvements are shown to hold in both cases. |
| title | Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2410.15161 |