Abstract
An associative brain-computer-interface (BCI) that correlates in time a peripherally generated afferent volley with the peak negativity (PN) of the movement related cortical potential (MRCP) induces plastic changes in the human motor cortex. However, in this associative BCI the movement timed to a cue is not detected in real time. Thus, possible changes in reaction time caused by factors such as attention shifts or fatigue will lead to a decreased accuracy, less pairings, and likely reduced plasticity. The aim of the current study was to compare the effectiveness of this associative BCI intervention on plasticity induction when the MRCP PN time is pre-determined from a training data set (BCIoffline), or detected online (BCIonline). Ten healthy participants completed both interventions in randomized order. The average detection accuracy for the BCIonline intervention was 71 ± 3% with 2.8 ± 0.7 min-1 false detections. For the BCIonline intervention the PN did not differ significantly between the training set and the actual intervention (t9 = 0.87, p = 0.41). The peak-to-peak motor evoked potentials (MEPs) were quantified prior to, immediately following, and 30 min after the cessation of each intervention. MEP results revealed a significant main effect of time, F(2,18) = 4.46, p = 0.027. The mean TA MEP amplitudes were significantly larger 30 min after (277 ± 72 μV) the BCI interventions compared to pre-intervention MEPs (233 ± 64 μV) regardless of intervention type and stimulation intensity (p = 0.029). These results provide further strong support for the associative nature of the associative BCI but also suggest that they likely differ to the associative long-term potentiation protocol they were modeled on in the exact sites of plasticity.
Highlights
IntroductionSince Daly et al (2009) proposed the possibility of a Brain-Computer-Interface (BCI) designed for neuromodulation of stroke patients, the field has rapidly expanded with numerous novel BCIs being introduced and tested in the clinic (Ang et al, 2010; Broetz et al, 2010; Cincotti et al, 2012; Li et al, 2013; Ramos-Murguialday et al, 2013; Mukaino et al, 2014; Young et al, 2014; Pichiorri et al, 2015; Mrachacz-Kersting et al, 2016)
The performance of the BCI in the BCIonline session for all participants expressed as true positive rate (TPR), true negative rate (TNR), false positive (FP), and false negative (FN) respectively, were 71 ± 3, 76 ± 5% and 2.8 ± 0.7, 3.1 ± 0.4 min−1
The aim of the current study was to compare the effects of an associative BCI intervention on plasticity induction when the movement related cortical potential (MRCP) peak negativity (PN) time is pre-determined from a training data set (BCIoffline), or detected online (BCIonline)
Summary
Since Daly et al (2009) proposed the possibility of a Brain-Computer-Interface (BCI) designed for neuromodulation of stroke patients, the field has rapidly expanded with numerous novel BCIs being introduced and tested in the clinic (Ang et al, 2010; Broetz et al, 2010; Cincotti et al, 2012; Li et al, 2013; Ramos-Murguialday et al, 2013; Mukaino et al, 2014; Young et al, 2014; Pichiorri et al, 2015; Mrachacz-Kersting et al, 2016). Only one group has investigated patients in the sub-acute phases of stroke (Mrachacz-Kersting et al, 2017b), presumably due to the relatively stable condition that a chronic stroke patient presents. BCIs function by collecting the brain signals during a specific state such as performing a movement or motor imagery, extracting features of interest and translating these into commands for external device control (Daly and Wolpaw, 2008). The available non-invasive BCIs for stroke patients have implemented both electroencephalography (EEG) or near-infrared spectroscopy (NIRS) to acquire the brain signals, extracted various spectral and temporal features [e.g., sensorimotor rhythm, movement related cortical potentials (MR)] and provided diverse types of afferent feedback to the patient such as those generated from using robotic devices, virtual reality or by driving direct nerve or muscular electrical stimulation (for review see, Cervera et al, 2018)
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