Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

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.

No abstract

A hybrid modality brain-computer interface (BCI) is proposed in this paper, which combines motor imagery with selective sensation to enhance the discrimination between left and right mental tasks, e.g., the classification between left/ right stimulation sensation and right/ left motor imagery. In this paradigm, wearable vibrotactile rings are used to stimulate both the skin on both wrists. Subjects are required to perform the mental tasks according to the randomly presented cues (i.e., left hand motor imagery, right hand motor imagery, left stimulation sensation or right stimulation sensation). Two-way ANOVA statistical analysis showed a significant group effect (F (2,20) = 7.17, p = 0.0045), and the Benferroni-corrected multiple comparison test (with α = 0.05) showed that the hybrid modality group is 11.13% higher on average than the motor imagery group, and 10.45% higher than the selective sensation group. The hybrid modality experiment exhibits potentially wider spread usage within ten subjects crossed 70% accuracy, followed by four subjects in motor imagery and five subjects in selective sensation. Six subjects showed statistically significant improvement ( Benferroni-corrected) in hybrid modality in comparison with both motor imagery and selective sensation. Furthermore, among subjects having difficulties in both motor imagery and selective sensation, the hybrid modality improves their performance to 90% accuracy. The proposed hybrid modality BCI has demonstrated clear benefits for those poorly performing BCI users. Not only does the requirement of motor and sensory anticipation in this hybrid modality provide basic function of BCI for communication and control, it also has the potential for enhancing the rehabilitation during motor recovery.

Electroencephalography based brain-computer interfaces (BCIs) show promise of providing an alternative communication channel between the brain and an external device. It is well acknowledged that BCI control is a skill and could be improved through practice and training. In this study, we explore the change of BCI behavioral performance as well as the electrophysiological properties across three training sessions in a pool of 42 human subjects. Our results show that the group average of BCI accuracy and the information transfer rate improved significantly in the third session compared to the first session; especially the significance reached in a smaller subset of a low BCI performance group (average accuracy <70%) as well. There was a significant difference of event-related desynchronization (ERD) lateralization for BCI control between the left- and right-hand imagination task in the last two sessions, but this significant difference was not revealed in the first training sessions. No significant change of R 2 value or event-related desynchronization and synchronization (ERD/ERS) for either channel C3 or channel C4, which were used for online control, was found across the training sessions. The change of ERD lateralization was also not significant across the training sessions. The present results indicate that BCI training could induce a change of behavioral performance and electrophysiological properties quickly, within just a few hours of training, distributed into three sessions. Multiple training sessions might especially be beneficial for the low BCI performers.

In this work, mechanical vibrotactile stimulation was applied to subjects’ left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation), and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation) were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement () from near 65% in motor imagery (with and without presence of stimulation) to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system.

No abstract

Mental fatigue is a commonly experienced state that can be induced by placing heavy demands on cognitive systems. This often leads to lowered productivity and increased safety risks. In this study, we developed a functional-connectivity based mental fatigue monitoring method. Twenty-six subjects underwent a 20-min mentally demanding test of sustained attention with high-resolution EEG monitoring. Functional connectivity patterns were obtained on the cortical surface via source localization of cortical activities in the first and last 5-min quartiles of the experiment. Multivariate pattern analysis was then adopted to extract the highly discriminative functional connectivity information. The algorithm used in the present study demonstrated an overall accuracy of 81.5% (p < 0.0001) for fatigue classification through leave-one-out cross validation. Moreover, we found that the most discriminative connectivity features were located in or across middle frontal gyrus and several motor areas, in agreement with the important role that these cortical regions play in the maintenance of sustained attention. This work therefore demonstrates the feasibility of a functional-connectivity-based mental fatigue assessment method, opening up a new avenue for modeling natural brain dynamics under different mental states. Our method has potential applications in several domains, including traffic and industrial safety.