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Original Research | Open Access

Hybrid SSVEP + P300 brain-computer interface can deal with non-stationary cerebral responses with the use of adaptive classification

Computer Engineering, Faculty of Engineering and Technology, Sankalchand Patel University, 384315 Visnagar, Gujarat, India
Department of Computer Science and Engineering, TGP College of Engineering and Technology, Nagpur University, 440033 Nagpur, Maharashtra, India
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The non-stationarity of electroencephalograms (EEG) has a substantial effect on the performance of classifiers in brain-computer interface (BCI) systems. To tackle this challenge, an adaptable version of the linear discriminant analysis (LDA) classifier was proposed. Accuracy is crucial when using BCIs for neurorestorative tasks or memory improvement. The accurate comprehension of brain responses facilitates more focused interventions, which may improve neurorestorative outcomes. BCIs equipped with adaptive classifiers are useful for personalizing therapies to individual requirements and for improving neurorestorative processes. Notably, neurorestorative interventions that yield consistent, accurate, and reliable outcomes are more likely to inspire trust and elicit satisfaction in users.


The proposed classifier continuously modified its parameters in accordance with EEG signals. The covariance matrix and mean values for each pair of classes were the updating parameters. The proposed classifier modified the model parameters by prioritizing current signal data over older signal history. The proposed classifier was tested using a hybrid SSVEP + P300 BCI system.

Results and conclusions

The proposed classifier had an estimated classification accuracy of 97.4%, and was more effective than pooled mean LDA and conventional multiclass LDA classifiers. Increased classification accuracy may increase the responsiveness of neurorestorative interventions and increase the usefulness of BCIs in neurorestoration.

Journal of Neurorestoratology
Article number: 100109
Cite this article:
Kapgate DD. Hybrid SSVEP + P300 brain-computer interface can deal with non-stationary cerebral responses with the use of adaptive classification. Journal of Neurorestoratology, 2024, 12(2): 100109.






Web of Science




Received: 26 December 2023
Revised: 15 February 2024
Accepted: 18 February 2024
Published: 13 March 2024
© 2024 The Author.

This is an open access article under the CC BY license (