Brain dynamics of assisted pedestrian navigation in the real-world
Wunderlich, A.; Gramann, K.
Show abstract
Conducting neuroscience research in the real world remains challenging because of movement- and environment-related artifacts as well as missing control over stimulus presentation. The present study demonstrated that it is possible to investigate the neuronal correlates underlying visuo-spatial information processing during real-world navigation. Using mobile EEG allowed for extraction of saccade- and blink-related potentials as well as gait-related EEG activity. In combination with source-based cleaning of non-brain activity and unfolding of overlapping event-related activity, brain activity of naturally behaving humans was revealed even in a complex and dynamic city environment.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Alteration of brain dynamics during natural dual-task walking 98%
- Mobile EEG reveals functionally dissociable dynamic processes supporting real-world ambulatory obstacle avoidance: Evidence for early proactive control 96%
- Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments 96%
Similar papers in this journal
- Neurophysiological Dynamics of Metacontrol States: EEG Insights into Conflict Regulation 96%
- Early beta oscillations in multisensory association areas underlie crossmodal performance enhancement 96%
- Characterization of neural communication dynamics in the Ventral Attention Network across distinct spatial and spatio-temporal scales 96%
Similar papers in this journal
- Investigating saccade-onset locked EEG signatures of face perception during free-viewing in a naturalistic virtual environment 97%
- The bodily appearance of a virtual partner affects the activity of the action observation and action monitoring systems in a minimally interactive task 96%
- Cross-validating the electrophysiological markers of early face categorization 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.