Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

Abstract

Brain-computer interfaces (BCIs) utilizing signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems significantly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly impact the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework utilizing electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the “brain” and “computer” components by respectively increasing user engagement through a continuous pursuit task and associated training paradigm, and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by over 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Finally, this framework was deployed in a physical task, demonstrating a near seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development and implementation of neurorobotics by means of noninvasive BCI.

Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

“…Whether the increased in alpha power over the motor cortex results from radiating alpha sources or traveling waves within or between disparate metastable brain states is as yet unknown (Vidaurre et al 2018;Zhang et al 2018;Roberts et al 2019). Regardless, since (1) most continuous BCI paradigms utilize rest as a state to be detected and (2) the decoder incorporates brain activity from both rest and motor imagery conditions in its decisions (i.e., there are not separate classifiers for rest and motor imagery), changing brain activity in one task improves classification of both conditions (evinced by the significant improvements in both 1D and 2D BCI control) (Perdikis et al 2018;Edelman et al 2019).…”

“…One promising approach attempts to decipher motor imagery from the electroencephalogram (EEG) by recording sensorimotor rhythms (SMRs), which predictably change in response to real and imagined movements (Pfurtscheller and Neuper 2001;He et al 2013). The intuitive and continuous nature of SMR-based BCIs enables the extension of the user through the control of virtual objects, drones, wheelchairs, and robotic arms Tsui et al 2011;Lafleur et al 2013;Edelman et al 2019). Users, for example, might imagine turning a steering wheel with their left hand to direct a wheelchair to the left or drink independently with a robotic arm after imagining reaching for a cup.…”

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

“…Motor variability due to variability in human kinematic parameters, e.g., force field adaptation, speed and trajectory, and motivational factors such as level of user engagement, arousal and feelings of competence, necessary for performing a motor task is an integral part of the motor learning process (Duarte and Reinkensmeyer, 2015;Úbeda et al, 2015;Edelman et al, 2019;Faller et al, 2019). Such variability does not necessarily represent noise contents only, but may potentially be a manifestation of motor and perceptual learning processes.…”

The future of upper extremity rehabilitation robotics: research and practice

“…The least invasive is electroencephalography (EEG), which uses surface electrodes placed on the scalp. EEG has been mainly used for assistive technology such as typing devices, but has more recently demonstrated capabilities of controlling a robotic hand . A more invasive technology is electrocorticography (ECoG), where electrodes are placed either above or below the dura mater layer .…”