%A Xu,Ren %A Jiang,Ning %A Mrachacz-Kersting,Natalie %A Dremstrup,Kim %A Farina,Dario %D 2016 %J Frontiers in Neuroscience %C %F %G English %K Brain-computer interface,motor intention detection,ballistic and repetitive task,Movement-related cortical potential,sensory-motor rhythm. %Q %R 10.3389/fnins.2015.00527 %W %L %M %P %7 %8 2016-January-21 %9 Original Research %+ Dario Farina,Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center,Göttingen, Germany,dario.farina@bccn.uni-goettingen.de %# %! Factors of Influence on a Short-Latency Brain Switch %* %< %T Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation %U https://www.frontiersin.org/articles/10.3389/fnins.2015.00527 %V 9 %0 JOURNAL ARTICLE %@ 1662-453X %X Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e., movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate (TPR) was ~70% for a detection latency of ~200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation.