Reducing Calibration Time Using Novel Hybrid Transfer-Learning for P300-Based BCI Applications

Sepideh Kilani, Seyedeh Nadia Aghili Kordmahale, Zabih Ghassemlooy, Mircea Hulea, Qiang Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


P300 is an event-related potential determined by the changes in natural neurons activity, which occurs mainly as a response to the infrequent stimuli. Considering that the positive potential can be monitored by non-invasive methods such as electroencephalogram, and that the 'oddball' paradigm elicits deliberately this response, P300 can be used in brain-computer interfaces (BCI). P300-based BCI applications suffer from the subject dependency problem, which is one crucial issue in the real-time implementation, requiring time-consuming calibration and a large number of training samples for learning the model. In this paper, a new approach based on transfer learning to overcome these problems is proposed, where the fine-tuning ability of a deep neural network for high-level feature extraction is being used. Euclidean space data alignment approach is adopted to make feature extraction data give similar distributions. Finally, transferred features are applied to a single-layer discriminative restricted Boltzmann machine for P300 detection. We have used a state-of-the-art dataset (BCI Competition III dataset II) for evaluating the proposed method. The results show that the proposed technique offers robust performance using a small number of training samples compared to the previous studies.

Original languageEnglish
Title of host publication2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)
Number of pages5
ISBN (Electronic)9781665410441
ISBN (Print)9781665410458
Publication statusPublished - Dec 2022

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