Event-related electric microstates of the brain differ between words with visual and abstract meaning

The present study shows that different neural activity during mental imagery and abstract mentation can be assigned to well-defined steps of the brain's information-processing. During randomized visual presentation of single, imagery-type and abstract-type words, 27 channel event-related potential (ERP) field maps were obtained from 25 subjects (sequence-divided into a first and second group for statistics). The brain field map series showed a sequence of typical map configurations that were quasi-stable for brief time periods (microstates). The microstates were concatenated by rapid map changes. As different map configurations must result from different spatial patterns of neural activity, each microstate represents different active neural networks. Accordingly, microstates are assumed to correspond to discrete steps of information-processing. Comparing microstate topographies (using centroids) between imagery- and abstract-type words, significantly different microstates were found in both subject groups at 286–354 ms where imagery-type words were more right-lateralized than abstract-type words, and at 550–606 ms and 606–666 ms where anterior-posterior differences occurred. We conclude that language-processing consists of several, well-defined steps and that the brain-states incorporating those steps are altered by the stimuli's capacities to generate mental imagery or abstract mentation in a state-dependent manner.

Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts

Prompted reports of recall of spontaneous, conscious experiences were collected in a no-input, no-task, no-response paradigm (30 random prompts to each of 13 healthy volunteers). The mentation reports were classified into visual imagery and abstract thought. Spontaneous 19-channel brain electric activity (EEG) was continuously recorded, viewed as series of momentary spatial distributions (maps) of the brain electric field and segmented into microstates, i.e. into time segments characterized by quasi-stable landscapes of potential distribution maps which showed varying durations in the sub-second range. Microstate segmentation used a data-driven strategy. Different microstates, i.e. different brain electric landscapes must have been generated by activity of different neural assemblies and therefore are hypothesized to constitute different functions. The two types of reported experiences were associated with significantly different microstates (mean duration 121 ms) immediately preceding the prompts; these microstates showed, across subjects, for abstract thought (compared to visual imagery) a shift of the electric gravity center to the left and a clockwise rotation of the field axis. Contrariwise, the microstates 2 s before the prompt did not differ between the two types of experiences. The results support the hypothesis that different microstates of the brain as recognized in its electric field implement different conscious, reportable mind states, i.e. different classes (types) of thoughts (mentations); thus, the microstates might be candidates for the `atoms of thought'.

Demo Abstract: Disseminating WMSN Data by Using Social Network and Web

For smart cities applications, a key requirement is to disseminate data collected from both scalar and multimedia wireless sensor networks to thousands of end-users. Furthermore, the information must be delivered to non-specialist users in a simple, intuitive and transparent manner. In this context, we present Sensor4Cities, a user-friendly tool that enables data dissemination to large audiences, by using using social networks, or/and web pages. The user can request and receive monitored information by using social networks, e.g., Twitter and Facebook, due to their popularity, user-friendly interfaces and easy dissemination. Additionally, the user can collect or share information from smart cities services, by using web pages, which also include a mobile version for smartphones. Finally, the tool could be configured to periodically monitor the environmental conditions, specific behaviors or abnormal events, and notify users in an asynchronous manner. Sensor4Cities improves the data delivery for individuals or groups of users of smart cities applications and encourages the development of new user-friendly services.

Invited Abstract: Issues with State-based Energy Consumption Modelling

Energy consumption modelling by state based approaches often assume constant energy consumption values in each state. However, it happens in certain situations that during state transitions or even during a state the energy consumption is not constant and does fluctuate. This paper discusses those issues by presenting some examples from wireless sensor and wireless local area networks for such cases and possible solutions.