Abstract
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.
Highlights
In the past decade, non-invasive brain-computer interfacing (BCI) based assistive technology has been proposed as a novel rehabilitation tool for people suffering of motor disorders (Daly and Wolpaw, 2008; Shih et al, 2012), such as stroke (Ramos-Murguialday et al, 2013) and spinal cord injury (Enzinger et al, 2008)
MRCPs have been proven as a promising signal type, for neuromodulation purposes due to its short detection latency (Mrachacz-Kersting et al, 2012; Niazi et al, 2012; Xu et al, 2014b)
We demonstrated that ballistic motor imagery task, frequency band of MRCP, and time-series analysis is the optimal combination in terms of detection performance
Summary
Non-invasive brain-computer interfacing (BCI) based assistive technology has been proposed as a novel rehabilitation tool for people suffering of motor disorders (Daly and Wolpaw, 2008; Shih et al, 2012), such as stroke (Ramos-Murguialday et al, 2013) and spinal cord injury (Enzinger et al, 2008). In BCI systems for neurorehabilitation, the volition of subjects is Factors of Influence on a Short-Latency Brain Switch detected from brain signals. Such a brain switch is used to control a neuroprosthetic device, such as an electrical stimulator (Niazi et al, 2012; King et al, 2014) or a robotic system (Xu et al, 2014b), to close the sensory-motor loop for either restoring motor function or modulating neural pathways (Shih et al, 2012). As the first step of these closed-loop systems, accurate online detection of motor intention is a crucial and challenging task for non-invasive neural recordings such as scalp electroencephalography (EEG), mainly due to its low spatial resolution and poor signal-to-noise ratio. The efficiency of plasticity induction would be extremely slow, if at all possible, when the artificially afferent triggered by the brain switch arrived at the cortical level either too early or too late relative to motor intention (Mrachacz-Kersting et al, 2012)
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