Real-time feedback improves imagined 3D primitive object classification from EEG

Attila Korik, Naomi du Bois, Gerard Campbell, Eamonn O'Neill, Laura Hay, Sam Gilbert, Madeleine Grealy, Damien Coyle

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Brain-computer interfaces (BCI) enable movement-independent information transfer from humans to computers. Decoding imagined 3D objects from electroencephalography (EEG) may improve design ideation in engineering design or image reconstruction from EEG for application in brain-computer interfaces, neuro-prosthetics, and cognitive neuroscience research. Object-imagery decoding studies, to date, predominantly employ functional magnetic resonance imaging (fMRI) and do not provide real-time feedback. We present four linked studies in a study series to investigate: (1) whether five imagined 3D primitive objects (sphere, cone, pyramid, cylinder, and cube) could be decoded from EEG; and (2) the influence of real-time feedback on decoding accuracy. Studies 1 (N = 10) and 2 (N = 3) involved a single-session and a multi-session design, respectively, without real-time feedback. Studies 3 (N = 2) and 4 (N = 4) involved multiple sessions, without and with real-time feedback. The four studies involved 69 sessions in total of which 26 sessions were online with real-time feedback (15,480 trials for offline and at least 6,840 trials for online sessions in total). We demonstrate that decoding accuracy over multiple sessions improves significantly with biased feedback (p = 0.004), compared to performance without feedback. This is the first study to show the effect of real-time feedback on the performance of primitive object-imagery BCI.
Original languageEnglish
Number of pages25
JournalBrain-Computer Interfaces
Early online date27 Mar 2024
Publication statusE-pub ahead of print - 27 Mar 2024


  • brain-computer interface (BCI)
  • imagined 3D objects
  • real-time signal processing
  • filter-bank common spatial patterns (FBCSP)
  • electroencephalography (EEG)


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