Designing a Clinically Viable Brain Computer Interface for the Control of Neuroprosthetics

Currently no brain computer interfaces exist that can control the individual fingers of a hand prosthesis and is suitable for permanent implantation in and individual with a single limb amputation. Within this thesis a design for a novel minimally invasive brain computer interface system is proposed that would be relatively low risk, allow for control of a prosthesis using existing cortical structures and be suitable for patients with loss of a single limb. The early stage development and proof of concept work has been done taking into account relevant regulatory requirements, so that a finalised version of the design would be suitable for regulatory certification. This novel design is found to be worth pursuing and may in turn open up new research opportunities

Decoding neural activity in sulcal and white matter areas of the brain to accurately predict individual finger movement and tactile stimuli of the human hand

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions

Evaluation of head-free eye tracking as an input device for air traffic control

International audienceThe purpose of this study was to investigate the possibility to integrate a free head motion eye-tracking system as input device in air traffic control (ATC) activity. Sixteen participants used an eye tracker to select targets displayed on a screen as quickly and accurately as possible. We assessed the impact of the presence of visual feedback about gaze position and the method of target selection on selection performance under different difficulty levels induced by variations in target size and target-to-target separation. We tend to consider that the combined use of gaze dwell-time selection and continuous eye-gaze feedback was the best condition as it suits naturally with gaze displacement over the ATC display and free the hands of the controller, despite a small cost in terms of selection speed. In addition, target size had a greater impact on accuracy and selection time than target distance. These findings provide guidelines on possible further implementation of eye tracking in ATC everyday activity

State-of-Science Review: SR-E29, Brain-Computer Interfaces and Cognitive Neural Prostheses

This review looks at recently developed technology that allows engineers to record signals from the brain, identify the subject’s intent, and allow the subject to control prosthetic devices or communicate with others. It explores the current status of the technology, focusing on studies aimed at developing assistive devices for human subjects. Lastly, it reviews the impressive accomplishments to date, as well as limitations of the technology that will need to be overcome to enable the development of fully practical assistive technologies

Recent Developments and Future Challenges in Medical Mixed Reality

As AR technology matures, we have seen many applicationsemerge in entertainment, education and training. However, the useof AR is not yet common in medical practice, despite the great po-tential of this technology to help not only learning and training inmedicine, but also in assisting diagnosis and surgical guidance. Inthis paper, we present recent trends in the use of AR across all med-ical specialties and identify challenges that must be overcome tonarrow the gap between academic research and practical use of ARin medicine. A database of 1403 relevant research papers publishedover the last two decades has been reviewed by using a novel re-search trend analysis method based on text mining algorithm. Wesemantically identified 10 topics including varies of technologiesand applications based on the non-biased and in-personal cluster-ing results from the Latent Dirichlet Allocatio (LDA) model andanalysed the trend of each topic from 1995 to 2015. The statisticresults reveal a taxonomy that can best describes the developmentof the medical AR research during the two decades. And the trendanalysis provide a higher level of view of how the taxonomy haschanged and where the focus will goes. Finally, based on the valu-able results, we provide a insightful discussion to the current limi-tations, challenges and future directions in the field. Our objectiveis to aid researchers to focus on the application areas in medicalAR that are most needed, as well as providing medical practitioners with latest technology advancements

Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience

Background: Implantable brain–computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation. Methods Two participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machinelearning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks. Results: Unsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%– 100.00%) (trial mean (median, Q1–Q3)) at a rate of 13.81 (13.44, 10.96–16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%–100.00%) at 20.10 (17.73, 12.27–26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants. Conclusion: We describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.Thomas J Oxley, Peter E Yoo, Gil S Rind, Stephen M Ronayne, C M Sarah Lee, Christin Bird, Victoria Hampshire, Rahul P Sharma, Andrew Morokoff, Daryl L Williams, Christopher MacIsaac, Mark E Howard, Lou Irving, Ivan Vrljic, Cameron Williams, Sam E John, Frank Weissenborn, Madeleine Dazenko, Anna H Balabanski, David Friedenberg, Anthony N Burkitt, Yan T Wong, Katharine J Drummond, Patricia Desmond, Douglas Weber, Timothy Denison, Leigh R Hochberg, Susan Mathers, Terence J O, Brien, Clive N May, J Mocco, David B Grayden, Bruce C V Campbell, Peter Mitchell, Nicholas L Opi

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes

Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework

Speech Neuroprostheses have the potential to enable communication for people with dysarthria or anarthria. Recent advances have demonstrated high-quality text decoding and speech synthesis from electrocorticographic grids placed on the cortical surface. Here, we investigate a less invasive measurement modality in three participants, namely stereotactic EEG (sEEG) that provides sparse sampling from multiple brain regions, including subcortical regions. To evaluate whether sEEG can also be used to synthesize high-quality audio from neural recordings, we employ a recurrent encoder-decoder model based on modern deep learning methods. We find that speech can indeed be reconstructed with correlations up to 0.8 from these minimally invasive recordings, despite limited amounts of training data

Constrained Motion Planning System for MRI-Guided, Needle-Based, Robotic Interventions

In needle-based surgical interventions, accurate alignment and insertion of the tool is paramount for providing proper treatment at a target site while minimizing healthy tissue damage. While manually-aligned interventions are well-established, robotics platforms promise to reduce procedure time, increase precision, and improve patient comfort and survival rates. Conducting interventions in an MRI scanner can provide real-time, closed-loop feedback for a robotics platform, improving its accuracy, yet the tight environment potentially impairs motion, and perceiving this limitation when planning a procedure can be challenging. This project developed a surgical workflow and software system for evaluating the workspace and planning the motions of a robotics platform within the confines of an MRI scanner. 3D Slicer, a medical imaging visualization and processing platform, provided a familiar and intuitive interface for operators to quickly plan procedures with the robotics platform over OpenIGTLink. Robotics tools such as ROS and MoveIt! were utilized to analyze the workspace of the robot within the patient and formulate the motion planning solution for positioning of the robot during surgical procedures. For this study, a 7 DOF robot arm designed for ultrasonic ablation of brain tumors was the targeted platform. The realized system successfully yielded prototype capabilities on the neurobot for conducting workspace analysis and motion planning, integrated systems using OpenIGTLink, provided an opportunity to evaluate current software packages, and informed future work towards production-grade medical software for MRI-guided, needle-based robotic interventions