Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients

Abstract

Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients. Invasive measurements of cortical currents using electrocorticography have been widely used for neuroprosthetic control. Moreover, magnetoencephalography (MEG) exhibits characteristic brain signals similar to those of invasively measured signals. However, it remains unclear whether non-invasively measured signals convey enough motor information to control a neuroprosthetic hand, especially for severely paralysed patients whose sensorimotor cortex might be reorganized. We tested an MEG-based neuroprosthetic system to evaluate the accuracy of using cortical currents in the sensorimotor cortex of severely paralysed patients to control a prosthetic hand. The patients attempted to grasp with or open their paralysed hand while the slow components of MEG signals (slow movement fields; SMFs) were recorded. Even without actual movements, the SMFs of all patients indicated characteristic spatiotemporal patterns similar to actual movements, and the SMFs were successfully used to control a neuroprosthetic hand in a closed-loop condition. These results demonstrate that the slow components of MEG signals carry sufficient information to classify movement types. Successful control by paralysed patients suggests the feasibility of using an MEG-based neuroprosthetic hand to predict a patient’s ability to control an invasive neuroprosthesis via the same signal sources as the non-invasive method.

Induced sensorimotor brain plasticity controls pain in phantom limb patients

“…The MEG signals during the offline task were decoded with accuracy comparable to that of previous work, demonstrating the successful online control of a robotic hand with accuracy greater than that of chance23. BMI training with the robotic hand was shown to induce robust changes in cortical activity and pain according to the type of the decoder.…”

“…The total number of patients was chosen to be comparable to that of previous studies and was based on our preliminary results for healthy controls trained by the same BMI prosthesis610. Notably, 9 of the 10 were the same patients who also took part in our previous study23. The study adhered to the Declaration of Helsinki and was performed in accordance with protocols approved by the Ethics Committee of Osaka University Clinical Trial Center (no.…”

“…At the beginning of the training, the experimenter changed the threshold to detect the onset. Because the optimal threshold estimated from the offline task was sometimes lower than the estimated values of the onset detector during resting in the online task, we changed the threshold values to not detect the onset during the resting state in the online task, although the other parameters estimated from the offline task were not changed23. The selected parameters were fixed for the 10 min of training.…”

“…A BMI works by first decoding neural activity of the mental action to move the affected hand, for example, and then by converting the decoded phantom hand movement into that of a robotic neuroprosthesis. BMIs based on magnetoencephalography (MEG) sensorimotor cortex signals have been shown to be sufficient to precisely decode hand movements in real time2021, even in severely paralysed patients simply intending to move the affected hands2223. Moreover, training to use BMIs induces plastic changes in cortical activity2425 and, potentially, associated clinical symptoms26.…”

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

“…To the best of our knowledge, few research groups have attempted control of a prosthetic or a robotic arm using scalp EEG based BCIs. A variety of control signals, including sensorimotor rhythms18, steady state visual evoked potentials1920, hybrid systems21, real movement or attempted movement2223, have been used for these initial studies to control the robotic or prosthetic arm. Such previous efforts have primarily constrained the BCI control system to be discrete in one dimension or a plane without exploring the full possibility of controls in three-dimensional space.…”