Research on brain-computer interfaces (BCIs) has been around for decades and recently the inner speech paradigm was picked up in the area. The realization of a functioning BCI could improve the life quality of many people, especially persons affected by Locked-In-Syndrome or similar illnesses. Although implementing a working BCI is too large of a commitment for a master's thesis, this thesis will focus on investigating machine learning methods to decode inner speech using data collected from the non-invasive and portable method electroencephalography (EEG). Among the methods investigated are three CNN architectures and transfer learning. The results show that the EEGNet architecture consistently reaches high classification accuracies, with the best model achieving an accuracy of 29.05%.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-91296 |
| Date | January 2022 |
| Creators | Jonsson, Lisa |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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