Abstract
Identifying unique descriptors for primary colours in EEG signals will open the way to Brain-Computer Interface (BCI) systems that can control devices by exposure to primary colours. This study is aimed to identify such unique descriptors in visual evoked potentials (VEPs) elicited in response to the exposure to primary colours (RGB: red, green, and blue) from 31 subjects. For that, we first created a classification method with integrated transfer learning that can be suitable for an online setting. The method classified between the three RGB classes for each subject, and the obtained average accuracy over 23 subjects was 74.48%. 14 out of 23 subjects were above the average level and the maximum accuracy was 93.42%. When cross-session transfer learning was evaluated, 86% of the subjects tested showed an average variation of 5.0% in the accuracy comparing with the source set.
S. Ludvigsen and E. H. Buøen—Equal contribution.
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Notes
- 1.
Accuracy = \(33\% + \frac{1}{3} 66\% = 55\%\).
- 2.
Accuracy = \(66\% + \frac{1}{3} 33\% = 77\%\).
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Ludvigsen, S.L., Buøen, E.H., Soler, A., Molinas, M. (2021). Searching for Unique Neural Descriptors of Primary Colours in EEG Signals: A Classification Study. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_26
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