Brain-Computer Interface Controlled Functional Electrical Stimulation System for Ankle Movement

Abstract

BackgroundMany neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals.MethodsA noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot.ResultsFive able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm.ConclusionsThis study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications, the proposed BCI-FES system may offer a novel and effective therapy in the neuro-rehabilitation of individuals with lower extremity paralysis due to neurological injuries.

Short-term dynamics of causal information transfer in thalamocortical networks during natural inputs and microstimulation for somatosensory neuroprosthesis

“…As has been confirmed with behavioral experiments (Talwar et al, 2002; Fitzsimmons et al, 2007; London et al, 2008; Do et al, 2011, 2012; Semprini et al, 2012), MiSt has the potential to be used as sensory feedback for closed-loop brain-machine-interface (BMI) devices (Vato et al, 2012; Liao et al, 2013). However, the short- and long-term effects of such artificial input to the brain have not been fully described.…”

Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery

“…However, detection of movement onset from EEG can be highly variable, ranging at least 100–300 ms [57,58]. Reliability can be improved with longer sampling times, but at the expense of even greater latency [59]. It is worth asking whether the apparent decreased sensitivity to timing [50,51] in these experiments compared to STDP or PAS (Figure 2) is simply due to the imprecision of movement detection by EEG.…”

Preliminary Study on Analyzing EEG Alpha Brainwave Signal Activities Based on Visual Stimulation

“…The EEGs are affected due to different degree of alertness as example the separate sleeping periods result in different EEG characteristics. There are several techniques proposed by Do et al [1], Domino et al [2], and Jacobs & Friedman [3], in integration the EEG and FES and the challenging of EEG signals mainly due to its small amplitude. The EEG signal passes through dura, cerebrospinal fluid and skull to scalp will produces peak-to-peak amplitude is only about 1 ~ 100 V with frequency range 0.5 ~ 100 Hz.…”