MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM


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Yılmaz Ç. M., Yilmaz B. H., Köse C.

Neural Computing and Applications, vol.37, no.6, pp.4679-4696, 2025 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 6
  • Publication Date: 2025
  • Doi Number: 10.1007/s00521-024-10917-5
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.4679-4696
  • Keywords: Brain–computer interface, Brain–mobile phone interface, Electroencephalography, Motor imagery
  • Karadeniz Technical University Affiliated: Yes

Abstract

EEG-based interfaces are an active research area with great potential. We, therefore, focused on classifying motor imaging (MI) tasks from various problem areas. Because of that, we applied MI patterns to voting ensembles differently and constructed voters. They employ quasi-probabilistic distribution models based on sub-classifiers of different frequency bands and time segments. Much previous work focused on just a few MI tasks for BCIs. To that end, we constructed a new mobile EEG dataset, abbreviated as MI-BMPI, containing two significant gestures for mobile phone interfaces. The research experiments used a consumer market EEG system, the mobile wireless Emotiv EPOC Flex neuroheadset. Experiments were carried out on the BCI Competition IV Dataset 2a and MI-BMPI. On the BCI and BMPI datasets, promising results were obtained in classifying various MI tasks. In conclusion, new solutions were introduced for tougher EEG-based interfaces, which have the potential to classify MI tasks and develop EEG-based interfaces. In addition to the average performance improvements, more stable results were achieved for subject and task variations.