Learning to control a BMI-driven wheelchair for people with severe tetraplegia.

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.

Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease.

Disruption of subthalamic nucleus dynamics in Parkinson's disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson's disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson's disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson's disease.

User Adaptation to Closed-Loop Decoding of Motor Imagery Termination.

One of the most popular methods in non-invasive brain machine interfaces (BMI) relies on the decoding of sensorimotor rhythms associated to sustained motor imagery. Although motor imagery has been intensively studied, its termination is mostly neglected. OBJECTIVE: Here, we provide insights in the decoding of motor imagery termination and investigate the use of such decoder in closed-loop BMI. METHODS: Participants (N = 9) were asked to perform kinesthetic motor imagery of both hands simultaneously cued with a clock indicating the initiation and termination of the action. Using electroencephalogram (EEG) signals, we built a decoder to detect the transition between event-related desynchronization and event-related synchronization. Features for this decoder were correlates of motor termination in the upper µ and ß bands. RESULTS: The decoder reached an accuracy of 76.2% (N = 9), revealing the high robustness of our approach. More importantly, this paper shows that the decoding of motor termination has an intrinsic latency mainly due to the delayed appearance of its correlates. Because the latency was consistent and thus predictable, users were able to compensate it after training. CONCLUSION: Using our decoding system, BMI users were able to adapt their behavior and modulate their sensorimotor rhythm to stop the device (clock) accurately on time. SIGNIFICANCE: These results show the importance of closed-loop evaluations of BMI decoders and open new possibilities for BMI control using decoding of movement termination.

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection.

Brain-machine interfaces have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography. Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This paper presents the decoding of lower-limb movement-related cortical potentials with continuous classification and asynchronous detection. We executed experiments in a customized gait trainer, where 10 healthy subjects performed self-initiated ankle plantar flexion. We further analyzed the features, evaluated the impact of the limb side, and compared the proposed framework with other typical decoding methods. No significant differences were observed between the left and right legs in terms of neural signatures of movement and classification performance. We obtained a higher true positive rate, lower false positives, and comparable latencies with respect to the existing online detection methods. This paper demonstrates the feasibility of the proposed framework to build a closed-loop gait trainer. Potential applications include gait training neurorehabilitation in clinical trials.

Brain-actuated gait trainer with visual and proprioceptive feedback.

OBJECTIVE: Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension. APPROACH: We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects. MAIN RESULTS: (i) For real-time classification, the average accuracy was [Formula: see text]% and [Formula: see text]% for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback ([Formula: see text]%) was significantly better than with visual feedback ([Formula: see text]%), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn. SIGNIFICANCE: The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that proprioceptive feedback has an advantage over visual feedback, which could be used to improve robot-assisted strategies for motor training and functional recovery.

Identification of genes related to Parkinson's disease using expressed sequence tags.

In a search for novel target genes related to Parkinson's disease (PD), two full-length cDNA libraries were constructed from a human normal substantia nigra (SN) and a PD patient's SN. An analysis of the gene expression profiles between them was done using the expressed sequence tags (ESTs) frequency. Data for the differently expressed genes were verified by quantitative real-time RT-PCR, immunohistochemical analysis and a cell death assay. Among the 76 genes identified with a significant difference (P > 0.9), 21 upregulated genes and 13 downregulated genes were confirmed to be differentially expressed in human PD tissues and/or in an MPTP-treated mice model by quantitative real-time RT-PCR. Among those genes, an immunohistochemical analysis using an MPTP mice model for alpha-tubulin including TUBA3 and TUBA6 showed that the protein levels are downregulated, as well as the RNA levels. In addition, MBP, PBP and GNAS were confirmed to accelerate cell death activity, whereas SPP1 and TUBA3 to retard this process. Using an analysis of ESTs frequency, it was possible to identify a large number of genes related to human PD. These new genes, MBP, PBP, GNAS, SPP1 and TUBA3 in particular, represent potential biomarkers for PD and could serve as useful targets for elucidating the molecular mechanisms associated with PD.

STARE: Spatio-Temporal Attention Relocation for Multiple Structured Activities Detection.

We present a spatio-temporal attention relocation (STARE) method, an information-theoretic approach for efficient detection of simultaneously occurring structured activities. Given multiple human activities in a scene, our method dynamically focuses on the currently most informative activity. Each activity can be detected without complete observation, as the structure of sequential actions plays an important role on making the system robust to unattended observations. For such systems, the ability to decide where and when to focus is crucial to achieving high detection performances under resource bounded condition. Our main contributions can be summarized as follows: 1) information-theoretic dynamic attention relocation framework that allows the detection of multiple activities efficiently by exploiting the activity structure information and 2) a new high-resolution data set of temporally-structured concurrent activities. Our experiments on applications show that the STARE method performs efficiently while maintaining a reasonable level of accuracy.

The immune modulation of Clara cell-10 in human peripheral monocytes and dendritic cells.

Although Clara cell secretory protein (CC-10, CC-16 or uteroglobin, secretoglobin 1A1) has been ascribed anti-inflammatory, immunomodulatory and anti-cancer activity roles in lung diseases including lung cancer, its precise function remains unclear. The objective of the present study was to evaluate the role of CC-10 in the immunomodulation of human monocytes and dendritic cells (DCs). The human lung adenocarcinoma cell line A549, was used to examine PGE2 production after cyclooxygenase (COX) inhibition and adenovirus encoding human CC-10 cDNA (Ad-CC-10) transfection. Type I and II cytokines were measured from peripheral blood mononuclear cells (PBMCs) and DCs which were cultured with tumor supernatant (TSN) or Ad-CC-10 transfected TSN. When PBMCs were cultured with supernatant A549 (tumor supernatant, TSN), the levels of T-cell helper type 1 (Th1) and 2 (Th2) cytokines increased. However, CC-10 inhibited the induction of Th2 cytokines of PBMCs stimulated with TSN. In DCs, TSN inhibited Th1 type cytokines but induced Th2 type. In contrast, TSN treated with either CC-10 or NS398 (COX-2 inhibitor) stimulated Th1 type and inhibited Th2 type without any phenotypic changes. The supernatants generated in the presence of NS-398 or CC-10 prevented tumor-induced inhibition of allogeneic T-cell stimulation. While the level of interleukin (IL)-10 secretion from DC-Ad-CC-10 was decreased, the level of IL-12 secretion was increased by CC-10. Collectively our data suggest that a supernatant of NSCLC causes an imbalance in the immune response of PBMCs and DCs, which is reversed by CC-10. This suggests that CC-10 is a candidate for the development of a new immunotherapy for lung cancer.