Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

Abstract

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project “Tremor” (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.

Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

“…1) Real EEG Data The brain computerized interface (BCI) system is utilized to calculate patients' EEG signals. The system has nine-channel sensors and 0-256 Hz sampling frequency [14]. Two forms of real EEG data are registered on 9 and 19 channels; for 9channel data, EEG signals are polluted with EOG and 50-Hz power line noise.…”

Therapeutic advances in tremor

“…Systems that attenuate tremor by delivering electrical stimulation to the antagonist muscles in an out‐of‐phase manner while recording tremor from the EMG signals of the muscles from which tremor originates have recently been tested in small open‐label studies in patients with PD or ET in which they produced marked reduction of tremor amplitude . The combination of functional electrical stimulation with a brain–computer interface that detects voluntary movement to trigger stimulation in a closed‐loop approach has also been also implemented by fusing signals from electroencephalogram, EMG, and kinematic sensors (gyroscopes and accelerometry) …”

Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients

“…Current technology available for clinical populations ranges from simple devices that stabilize a shaking hand (Popović Maneski et al, 2011 ; Grimaldi et al, 2013 ), to devices that augment the ability of a patient with locked-in syndrome to communicate with others (Holz et al, 2015 ). While these technologies offer promise for recovery from or for relief of symptoms of CNS injury, there are still many challenges in the integration of BCIs into effective prosthetic devices.…”