| Author | Moktan, Rakshya Lama |
| Call Number | AIT Thesis no.DSAI-25-02 |
| Subject(s) | Brain-computer interfaces
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| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | BCI spellers have made tremendous strides in recent years through the introduction of novel paradigms and improved development practices, yet their real-world deployment remains limited by low BCI literacy—very few users can consistently generate the neural signals required for reliable control. Because BCI literacy hinges on a constellation of cognitive and neurophysiological factors but remains largely under-investigated, we propose a comprehensive study to map its key determinants. In our protocol, we will assess user proficiency using both the P300 event–related potential and the Predictive Index Cognitive Assessment (PICA) test within a 16-target sub-speller hybrid config uration that combines P300 and SSVEP modalities. Leveraging a cost-effective gg.tec UnicornHybridBlackwire-less EEG headset, we will recruit 22 participants to evaluate how session timing, gender, P300 amplitude, cognitive skill scores, and SSVEP fidelity each influence speller accuracy. By elucidating these relationships—and demonstrating the performance gains of a sub-speller–based hybrid approach—our work aims to lay a practical foundation for developing robust, user-friendly, and accessible BCI spelling systems. |
| Year | 2025 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Chaklam Silpasuwanchai |
| Examination Committee(s) | Chantri Polprasert;Attaphongse Taparugssanagorn |
| Scholarship Donor(s) | His Majesty the King’s Scholarships (Thailand) |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2025 |