We present a dataset combining high-density Electroencephalography (HD-EEG, 128-channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18–33). Along with the dataset, we also provided the preliminary microstate analysis of resting-state EEG and the ERPs, topomap, and time-frequency maps of the task-related EEG. We believe that the simultaneous mouse-tracking and EEG recording would crack the core components of binary choices and further index the temporal dynamics of decision making and response hesitation. This publicly available dataset could support the development of neural signal processing methods in motor EEG, thus advancing research in both the decision neuroscience and brain-computer interface (BCI) applications.

We present a dataset combining high-density Electroencephalography (HD-EEG, 128-channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18–33). Along with the dataset, we also provided the preliminary microstate analysis of resting-state EEG and the ERPs, topomap, and time-frequency maps of the task-related EEG. We believe that the simultaneous mouse-tracking and EEG recording would crack the core components of binary choices and further index the temporal dynamics of decision making and response hesitation. This publicly available dataset could support the development of neural signal processing methods in motor EEG, thus advancing research in both the decision neuroscience and brain-computer interface (BCI) applications.