Classification of P300 from non-invasive EEG signal using convolutional neural network
| dc.contributor.author | Farhat, Nazia, author | |
| dc.contributor.author | Anderson, Charles W., advisor | |
| dc.contributor.author | Kirby, Michael, committee member | |
| dc.contributor.author | Blanchard, Nathaniel, committee member | |
| dc.date.accessioned | 2022-05-30T10:21:12Z | |
| dc.date.available | 2022-05-30T10:21:12Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Brain-Computer Interface system is a communication tool for the patients of neuromuscular diseases. The efficiency of such a system largely depends on the accurate and reliable detection of the brain signal employed in its operation. P300 Speller, a well-known BCI system, which helps the user select the desired alphabet in the communication process uses an Electroencephalography signal called P300 brain wave. The spatiotemporal nature and the low Signal-to-noise ratio along with the high dimensionality of P300 signal imposes difficulties in its accurate recognition. Moreover, its inter- and intra-subject variability necessitates case-specific experimental setup requiring considerable amount of time and resources before the system's deployment for use. In this thesis Convolutional Neural Network is applied to detect the P300 signal and observe the distinguishing features of P300 and non-P300 signals extracted by the neural network. Three different shapes of the filters, namely 1-D CNN, 2-D CNN, and 3-D CNN are examined separately to evaluate their detection ability of the target signals. Virtual channels created with three different weighting techniques are explored in 3-D CNN analysis. Both within-subject and cross-subject examinations are performed. Single trial accuracy with CNN implementation. Higher single trial accuracy is observed for all the subjects with CNN implementation compared to that achieved with Stepwise Linear Discriminant Analysis. Up to approximately 80% within-subject accuracy and 64% cross- subject accuracy are recorded in this research. 1-D CNN outperforms all the other models in terms of classification accuracy. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Farhat_colostate_0053N_17046.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235175 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.title | Classification of P300 from non-invasive EEG signal using convolutional neural network | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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