Journal of Neurology and Neuroscience

  • ISSN: 2171-6625
  • Journal h-index: 17
  • Journal CiteScore: 4.12
  • Journal Impact Factor: 3.21
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
Awards Nomination 20+ Million Readerbase
Indexed In
  • Open J Gate
  • Genamics JournalSeek
  • The Global Impact Factor (GIF)
  • China National Knowledge Infrastructure (CNKI)
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Proquest Summons
  • Scientific Journal Impact Factor (SJIF)
  • Euro Pub
  • Google Scholar
  • Secret Search Engine Labs
Share This Page


Cross-Subject and Cross-Paradigm learning using Convolutional Neural Network for P300 Event-Related Potential Detection

Amir Mohammad Mijani, Aref Einizade, Mohammad Bagher Shamsollahi and Behrad Taghi Beyglou

Background: P300 Speller systems conventionally are using an oddball pattern which results in P300 component. By using the P300 component, mentally disabled can spell different characters.One of the most disadvantages of ERP-based BCI systems, especially P300 spellers, is the need for a large amount of training data which is time-consuming and exhausting for users.
New method: The goals are to evaluate the possibility of Transfer Learning (TL) implementation using the finetuning technique on convolutional neural networks (CNN) by two different approaches: 1) Cross-Subject, and 2) Cross-Paradigm. In cross-subject, data of individual paradigm cross different subjects and in cross-paradigm, data of individual subject cross different paradigms were applied.
Results: The final results illustrate that the amount of network training data is reduced up to 75 percent. Furthermore, the average of character detection accuracy using CNN is increased 11.76%, 13.95% and 13.51% in cross-subject TL in comparison to LDA classifier for single, dual and triple paradigms respectively. Also, such accuracy is increased by 6.76%, 10.95% in cross-paradigm TL in comparison to LDA classifier for dual and triple paradigms respectively.
Comparison with existing methods: Cross-subject and the novel cross-paradigm suggested in this study reduce the amount of needed training data in comparison to existing subject-dependent methods. In addition, the performance overall was improved against LDA in TL condition.
Conclusions: Overall in cross-subject and cross-paradigm TL approach using CNN, character detection accuracy was improved in comparison to LDA and the amount of training data was decreased significantly.