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

Brain-computer interfaces (BCI) enable movement-independent information transfer from humans to 19 computers. Decoding imagined 3D objects from electroencephalography (EEG) may improve design 20 ideation in engineering design or image reconstruction from EEG for application in brain-computer 21 interfaces, neuro-prosthetics, and cognitive neuroscience research. Object-imagery decoding studies, to 22 date, predominantly employ functional magnetic resonance imaging (fMRI) and do not provide real-time 23 feedback. We present four linked studies in a study series to investigate: (1) whether five imagined 3D 24 primitive objects (sphere, cone, pyramid, cylinder, and cube) could be decoded from EEG; and (2) the 25 influence of real-time feedback on decoding accuracy. Studies 1 (N=10) and 2 (N=3) involved a single-26 session and a multi-session design, respectively, without real-time feedback. Studies 3 (N=2) and 4 (N=4) 27 involved multiple sessions, without and with real-time feedback. The four studies involved 69 sessions in 28 total of which 26 sessions were online with real-time feedback (15,480 trials for offline and at least 6,840 29 trials for online sessions in total). We demonstrate that decoding accuracy over multiple sessions 30 improves significantly with biased feedback (p=0.004), compared to performance without feedback. This 31 is the first study to show the effect of real-time feedback on the performance of primitive object-imagery 32 BCI.