Classification of P300 from non-invasive EEG signal using convolutional neural network

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.

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2022

Authors dc:creator

• Farhat, Nazia, author
• Anderson, Charles W., advisor
• Kirby, Michael, committee member
• Blanchard, Nathaniel, committee member

Statement 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.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.02467

OAI identifier oai:identifier

oai:mountainscholar.org:10217/235175