Current Challenges for the Practical Application of Electroencephalography-Based Brain-Computer Interfaces

It has been almost 50 years since the term“brain–computer interface”(BCI)was first proposed by Jacques J.Vidal in 1973[1].Unlike traditional electronic interfaces that transmit nonliving information between devices,BCIs set up a communication bridge between a living brain and nonliving devices.Technically speaking,a BCI is a system that measures brain activity and converts it into the artificial outputs that replace,restore,enhance,supplement,or improve the natural central nervous system outputs[2].At present,electroencephalography(EEG)is the most commonly used brain signal for BCIs.

EEG-controlled functional electrical stimulation rehabilitation for chronic stroke:system design and clinical application

Stroke is one of the most serious diseases that threaten human life and health.It is a major cause of death and disability in the clinic.New strategies for motor rehabilitation after stroke are undergoing exploration.We aimed to develop a novel artificial neural rehabilitation system,which integrates brain--computer interface(BCI)and functional electrical stimulation(FES)technologies,for limb motor function recovery after stroke.We conducted clinical trials(including controlled trials)in 32 patients with chronic stroke.Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation(NMES)group.The changes in outcome measures during intervention were compared between groups,and the trends of ERD values based on EEG were analyzed for BCI-FES group.Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity(FMA-UE)and Kendall Manual Muscle Testing(Kendall MMT)scores of the BCI-FES group was significantly higher than that in the sham group,which indicated the practicality and superiority of the BCI-FES system in clinical practice.The change in the laterality coefficient(LC)values based onμ-ERD(ΔLCm-ERD)had high significant positive correlation with the change in FMA-UE(r=0.6093,P=0.012),which provides theoretical basis for exploring novel objective evaluation methods.

Dynamic Brain Responses Modulated by Precise Timing Prediction in an Opposing Process

The brain function of prediction is fundamental for human beings to shape perceptions efficiently and successively. Through decades of effort, a valuable brain activation map has been obtained for prediction. However,much less is known about how the brain manages the prediction process over time using traditional neuropsychological paradigms. Here, we implemented an innovative paradigm for timing prediction to precisely study the temporal dynamics of neural oscillations. In the experiment recruiting 45 participants, expectation suppression was found for the overall electroencephalographic activity,consistent with previous hemodynamic studies. Notably,we found that N1 was positively associated with predictability while N2 showed a reversed relation to predictability. Furthermore, the matching prediction had a similar profile with no timing prediction, both showing an almost saturated N1 and an absence of N2. The results indicate that the N1 process showed a ‘sharpening' effect for predictable inputs, while the N2 process showed a‘dampening' effect. Therefore, these two paradoxical neural effects of prediction, which have provoked wide confusion in accounting for expectation suppression,actually co-exist in the procedure of timing prediction but work in separate time windows. These findings strongly support a recently-proposed opposing process theory.