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Demirel, C.

Publications and source records attributed to Demirel, C..

2 recordsLinked to original sources

Electrophysiological correlates of lucid dreaming

Lucid dreaming (LD) is a state of conscious awareness of the current dream state, predominantly associated with REM sleep. Research progress in uncovering the neurobiological basis of LD has been hindered by low sample sizes, diverse EEG setups, and specific artifact issues like saccadic eye movements and signal non-stationarity. To address these matters, we developed a multi-stage preprocessing pipeline that integrates standardized early-stage preprocessing, artifact subspace reconstruction, and signal-space projection. This approach enhanced data quality by precisely removing saccadic potential effects even in setups with minimal channels. To robustly identify the electrophysiological correlates of LD, we applied this methodology to LD data collected across laboratories (pooled N = 44) and explored sensor-and source-level markers hypothesized to underlie LD. Compared to non-lucid REM sleep, we observed few robust differences on the EEG sensor level, which is in line with recent findings. In contrast, on the source level, gamma1 power (30-36 Hz) showed increases during LD in left-hemispheric temporal areas, which might reflect verbal insight processes. Gamma1 power also increased around the onset of LD eye signaling in right temporo-occipital regions including the right precuneus, in line with its involvement in self-referential thinking. Reductions in beta power (12-30 Hz) during LD in right central and parietal areas including the temporo-parietal junction are potentially associated with a conscious reassessment of the veridicality of the currently perceived reality. Notably, functional connectivity in alpha band (8-12 Hz) increased during LD, in contrast to the reductions typically seen in psychedelic states, highlighting enhanced self-awareness. Taken together, these findings illuminate the electrophysiological correlates of LD state, and may serve as a basis to uncover neural mechanisms at the time point of lucid dream insight.

neuroscience↗

Shaping Sounds with P300 Based Brain-Computer Musical Interface

This paper describes the development and testing of a brain-computer musical interface (BCMI) that allows a user to select and transform one element of a musical texture by paying attention to that particular element. In order to realize the BCMI system mentioned, a comprehensive testing scheme was established which uses auditory evoked potentials to elicit P300 waves via averaging various types of stimuli. Resented sound stimuli were divided into multi-channel speaker setups to have better localization of user-focused sound stimuli. A sound synthesis model was developed for transforming its sound texture based on neural oscillations that were categorized with the help of a self-organizing map algorithm. In addition, an artificial neural network was used to predict the possible P300 waves that show the attentional focus of a subject. Most of the P300 waves were classified successfully for most of the participants. Promising results were achieved concerning the developed BCMI system. A neural network model was also utilized to predict the possible P300 waves, which show the subjects selective attention. The majority of the participants were able to correctly classify P300 waves. The proposed BCMI system yielded promising results.

neuroscience↗