Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Abstract

Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.

Topographical polarity reveals continuous EEG microstate transitions and electric field direction in healthy aging

“…We labeled them as microstates A, B, C, D, and E by careful visual inspection based on the labels unified in Tarailis et al (2024) (Figure 1a). The spatial correlations between our templates and those of a previous study (Asai et al, 2022), which employed the four template maps A, B, C, and D, were sufficiently high (for microstates A–D, r s > .88; Figure 1b). Additionally, our microstate E had the highest correlation coefficient with microstate C among the four maps used by Asai et al (2022) ( r =.73), consistent with the findings of previous studies that microstates C and E had not previously been distinguished (e.g., Tarailis et al, 2021).…”

“…(b) Example of spatial correlation between our templates and those of other studies. We compared our templates with those of Asai et al (2022). The spatial correlation coefficients were computed using a vectorized 67*67 interpolated scalp map data matrix obtained using the EEGLAB function “topoplot” to absorb differences in the number of electrodes.…”

“…In extracting microstate templates as basis vectors at this point, we ignored topographical polarity, i.e., we only considered spatial configuration. For instance, the topographical polarity of microstate A in Asai et al (2022) was reversed from our microstate A, so we aligned both maps to positive topographical polarity and then calculated their spatial correlations for convenience. (c) We used the polarity-considered template maps.…”

“…We selected k = 5 as the optimal number of k according to the CV criteria. Labels A-E were assigned based on similarities with templates identified in Asai et al (2022) and through visual inspection based on Tarailis et al (2024), who unified microstate labels. (b) Example of spatial correlation between our templates and those of other studies.…”

“…Although our results represent only topographical transitions recorded from supracranial EEG, polarity-sensitive microstate analysis allows us to represent spatiotemporal continuous EEG transitions, which may reveal “easy/hard routes to transition” constrained by brain structure and network proximity. Recent discussions in cognitive neuroscience have shifted from the Sherringtonian approach, which elucidates the function of individual brain regions and networks based on functional classification of neuronal populations, to the Hopfieldian approach, which understands the neural basis of cognition from the transition dynamics of masses encompassing local activities (Asai et al, 2022; Barack, 2021; Barack & Krakauer, 2021; Ebitz & Hayden, 2021). The polarized microstate approach may be helpful in such a paradigm shift at the explanatory level.…”