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

Korik, Attila and du Bois, Naomi and Campbell, Gerard and O'Neill, Eamonn and Hay, Laura and Gilbert, Sam and Grealy, Madeleine and Coyle, Damien (2024) Real-time feedback improves imagined 3D primitive object classification from EEG. Brain-Computer Interfaces. ISSN 2326-2621 (https://doi.org/10.1080/2326263X.2024.2334558)

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Abstract

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.

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