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Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

Conventional rehabilitation strategies for stroke survivors become difficult when voluntary movements are severely disturbed. Combining passive limb mobilization, robotic devices and EEG-based brain-computer interfaces (BCI) systems might improve treatment and clinical follow-up of these patients, but detailed knowledge of neurophysiological mechanisms involved in functional recovery, which might help for tailoring stroke treatment strategies, is lacking. Movement-related EEG changes (EEG event-related desynchronization (ERD) in $beta$ and $alpha$ bands, an indicator of motor cortex activation traditionally used for BCI systems), were evaluated in a group of 23 paralyzed chronic stroke patients in two unilateral motor tasks alternating paretic and healthy hands ((i) passive movement, using a hand exoskeleton, and (ii) voluntary movement), and compared to nine healthy subjects. In tasks using unaffected hand, we observed an increase of contralesional hemisphere activation for stroke patients group. Unexpectedly, when using paralyzed hand, motor cortex activation was reduced or absent in severely affected group of patients, while patients with moderate motor deficit showed an activation greater than control group. Cortical activation was reduced or absent in damaged hemisphere of all the patients in both tasks. Significant differences related to severity of motor deficit were found in the time course of $alpha$-$beta$ bands power ratio in EEG of contralesional hemisphere while moving affected hand. These findings suggest the presence of different compensation mechanisms in contralesional hemisphere of stroke patients related to the grade of motor disability, that might turn quantitative EEG during a movement task, obtained from a BCI system controlling a robotic device included in a rehabilitation task, into a valuable tool for monitoring clinical progression, evaluating recovery, and tailoring treatment of stroke patients.

In the present work, we provide new arguments and data indicating that early ERP components are generated at least in part by evoked theta and alpha oscillations. We proceed from the general hypothesis, originally proposed by Erol Basar that ERP's are generated by a superposition of evoked oscillations with different frequencies. Based on findings about event-related desynchronization/synchronization (ERD/ERS), we analyze the following specific hypotheses. If evoked theta and alpha oscillations contribute to the generation of ERP components and if their functional role, type of reactivity and frequency specificity are similar to those of event-related oscillations (measured by ERD/ERS), we expect (i) to see the same functional relationship between frequency and cognitive processes, (ii) the same type of "reactivity" and a (iii) dependency of latency measures of evoked components on IAF. By reviewing respective data, we demonstrate that similar to research about event-related oscillations, evoked alpha reflects attention, whereas evoked theta reflects working memory processes. Furthermore, it was found that individual alpha frequency (IAF) has a significant influence on P1 latency in particular. For a better understanding of these findings, we outline a new theoretical framework. We assume that the P1–N1 complex is generated by an interplay between the synchronous activation of three neuronal network systems, a working memory, attentional, and semantic memory system, each operating with a different frequency, the first in the theta (about 6 Hz), the second in the lower alpha (about 8 Hz) and the third in the upper alpha (about 12 Hz) frequency range. The implications of this theoretical framework are discussed by reviewing research using phase sensitive measures to analyze "local" and "large scale" integration processes between different neural networks.

This work describes the development of a nonfatigating Brain–Computer Interface (BCI) based on Steady State Evoked Potentials (SSVEP) and Event-Related Desynchronization (ERD) to control an autonomous car. Through a graphical interface presented to the user in the autonomous car, destination places are shown. The selection of commands is performed through visual stimuli and brain signals. The signals are captured on the occipital region of the scalp, and are processed in order to obtain the necessary data for the planning system of the autonomous car. Test performed obtained success rate of 90% for a synchronous BCI and 83% for an asynchronous BCI. The proposed system is a hybrid-BCI, which includes the ability to enable and disable the visual stimuli, reducing fatigue associated with the use of SSVEP-based BCIs. The video showing this development can be accessed on: cbeb2020.org/AutonomousCarVideo.mp4.