Brain-Computer Interface Research

Brain-computer interfaces (BCIs) are devices that directly read brain activity and use it in a real-time, closed loop system with feedback to the user. Unlike all other interfaces, BCIs do not require movement. Instead, the information from the brain is translated into messages or commands without relying on the body’s natural output pathways. Thus, BCIs can be very helpful to people with severe motor disabilities that prevent them from speaking or using most (or even all) other devices for communication.

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. It has previously been shown that intracortically-recorded signals can be decoded to extract information related to movement, allowing non-human primates and paralyzed humans to control computers, wheelchairs and robotic arms through imagined movements. In non-human primates, these types of signals have also been used to drive activation of chemically paralyzed arm muscles. In an entirely novel application of brain computer interface (BCI) technology, we show that intracortically-recorded signals can be linked in real-time to muscle activation to restore functional wrist and finger movement in a paralyzed human. Our technology is designed to restore lost function and could be used to form an electronic ‘neural bypass’ to circumvent disconnected pathways in the nervous system.

The optimization of brain-computer interfaces (BCIs) will require the delivery of feedback signals to the somatosensory and/or proprioceptive cortices of the device user. Ultimately, the precision and reliability with which such signals can be delivered will underlie the quality and consistency of the information that can be conveyed. Unfortunately, the use of implantable micro-electrodes to deliver electrical signals directly into cortex has several fundamental limitations that limit their efficacy and can reduce their consistency over time. Magnetic stimulation from micro-coils overcomes these limitations, and so here, we describe the development of a cortically-implantable micro-coil and review some of the advantages of this approach.

Broad-band evoked potentials (BBEPs) are responses to non-periodic stimuli, evoked in reponse to carefully chosen pseudo-random noise-sequences (PRNS). In this chapter, a generative method called reconvolution is discussed. Reconvolution is a method that decomposes BBEPs in response to PRNS into transient responses to the individual events. With reconvolution, one can then generate responses to any other novel PRNS. In this chapter, we discuss three approaches to reconvolution: (1) a sequential approach, (2) an integrated approach, and (3) a zero-training approach. Reconvolution enables Brain Computer Interfaces to be trained with little or even no data, and provides a generative model to accurately predict responses to novel stimuli.

Dexterous object manipulation requires cutaneous sensory feedback, and in its absence, even simple grasping tasks appear clumsy and slow. In prosthetic limbs controlled through intracortical brain-computer interfaces (iBCIs), restoring this somatosensory feedback could be an important step to improving function as vision provides only impoverished cues during object interactions. Intracortical microstimulation (ICMS) of primary somatosensory cortex (S1) is a potential method to restore this sensory feedback, particularly in people who cannot benefit from stimulation of the peripheral nervous system. Here, we demonstrate the ability of ICMS delivered to S1 to produce somatotopically relevant, cutaneous percepts on individual fingers with graded intensity. This demonstrates the capabilities of ICMS for providing cutaneous feedback to iBCI users.

Intracranial electrode arrays for recording and stimulating electrical brain activity have facilitated major advances in the treatment of neurological conditions over the past decade. When compared to scalp electroencephalography (EEG), cortical recordings have demonstrated superior spatial resolution and consequently a greater potential for cognitive command output. Traditional cortical arrays require direct implantation into the brain via open craniotomy, which is a delicate and lengthy procedure. This can lead to inflammatory tissue responses amongst other clinical complications and has necessitated the development of minimally invasive methods that circumvent or mitigate brain trauma. In this study, we demonstrate the feasibility of chronically recording brain activity from within an external cerebral vein using a passive stent - electrode recording array (stentrode). We achieved implantation into a superficial cortical vein lying adjacent to the motor cortex using catheter angiography. Access was made via vascular puncture in the external jugular vein in the neck. Following successful implantation, we demonstrated neural recordings in freely moving sheep for time periods up to 190 days. Venous internal lumen patency was preserved for the duration of implantation. Spectral content and bandwidth of vascular electrocorticography were found to be comparable to those of recordings from epidural surface arrays.

A rat robot is a type of animal robot in which an animal is connected to a machine system via a brain-computer interface (BCI). Electrical stimuli can be generated by the machine system and delivered to the animal’s brain to control its behavior. However, most existing rat robots require that a human observes the environmental layout to guide navigation, which limits the applications of rat robots. This work incorporates object detection algorithms to a rat robot system to enable it to find ‘human-interesting’ objects, and then use these cues to guide its behaviors to perform automatic navigation. A miniature camera is mounted on the rat’s back to capture the scene in front of the rat. The video is transferred via a wireless module to a computer and we develop some object detection/identification algorithms to allow objects of interest to be found. Next, we make the rat robot perform a specific motion automatically in response to a detected object, such as turning left. A single stimulus does not allow the rat to perform a motion successfully. Inspired by the fact that humans usually give a series of stimuli to a rat robot, we develop a closed-loop model that issues a stimulus sequence automatically according to the state of the rat and the objects in front of it until the rat completes the motion successfully. Thus, the rat robot, which we refer to as a rat cyborg, is able to move according to the detected objects without requiring manual operations. The closed-loop stimulation model is evaluated in experiments, which demonstrate that our rat cyborg can accomplish human-specified navigation automatically.

We present two studies in which brain-computer interfaces (BCIs) used two related EEG signals, the readiness potential (RP) and the lateralized readiness potential (LRP), in order to predict and feed back motor intentions in real-time. In each of the studies, the experimental task was designed as a game that the subjects played against the BCI. In one of the experiments, subjects played a “duel” game against the BCI. They were challenged to perform spontaneous button presses but to withhold any movement when interrupted by a stop signal. This stop signal was controlled by the BCI that had been trained to predict movements by detecting the occurrence of RPs in the ongoing EEG. In the other experiment, participants played a “matching pennies” game. They won a point if they raised a different hand than the opponent at the end of a countdown and lost a point otherwise. The opponent was played by the BCI, who had been trained to predict from the LRP in the ongoing EEG which hand the subject would move at the end of the countdown. Hence, in both experiments a key feature of the BCI was its closed-loop nature, that is the ability to predict the motor intention in real-time and provide an immediate feedback of the prediction to the subject. In both experiments, prediction accuracies of the BCI were substantially higher compared to random predictions, thereby demonstrating the success of this approach. This allows researchers to use BCIs as research tools to address questions from cognitive neuroscience and provide new insights into the coupling of motor preparatory signals and the corresponding actions.

During brain surgery, functional brain mapping is critical, and the time needed for the procedure should be reduced to the minimum in order to avoid risks for the patient. In this project, we extend the traditional concept of BCI for communication and control between brain and external devices, to the concept of Brain-Surgeon Interface (BSI) in order to establish an interactive channel between the patient’s brain and the surgeon, with the ultimate goal of a high quality and precise brain surgery. Compared with “intraoperative fMRI”, which is expensive and time-consuming, or cortical electrical stimulation, which may cause epilepsy and brain swelling during the surgery, the proposed ECoG-based BSI system works in an online scenario, and the mapping time can be significantly reduced to a half minute for both motor and sensory cortex. Three signal modalities were used for functional brain mapping: movement related cortical potential, steady-state somatosensory evoked potential, event-related (de)synchronization. The proposed BSI system may provide a considerable advantage for clinical brain surgery applications.

We present successful results, based on testing with nine healthy users, demonstrating an innovative brain-computer interface (BCI) paradigm. The new paradigm utilizes a code- modulated visual evoked potential (cVEP), with a relatively high carrier frequency of 40 Hz (which is about the threshold that human vision can detect) using pseudo-random pattern flashing stimuli. These visual stimuli are very perceptually friendly and, due to their wide frequency spectral patterns, not prone to triggering epileptic seizures. To generate higher frequency stimulation than state-of-the-art steady-state visual evoked potential (SSVEP) or cVEP-based BCIs, we utilize the light-emitting diodes (LEDs) driven from an ARDUINO DUE board with a software generator designed by our team.

Patients diagnosed with complete locked in syndrome (CLIS) or a disorder of consciousness (DOC) have no reliable control of voluntary movements. Hence, assessing their cognitive functions and cognitive awareness can be challenging. The “gold standard” for such assessments relies on behavioral responses, and recent work using different neuroimaging methods has shown that behavioral diagnoses may underestimate patients’ capabilities. Thus, there is a pressing need for new methods that go beyond behavioral approaches and can help patients even if they are not able to produce any behavioral response. In one of the most prominent trends in brain-computer interface (BCI) research, many groups have been using BCI technology to provide a suite of approaches to assess cognition and consciousness using EEG-based tools. This paper presents results with P300, steady-state visual evoked potential (SSVEP) and motor imagery BCIs and other approaches with different target patients in several different real-world settings. Results confirm that EEG-based assessment can reveal details about patients’ remaining capabilities that can both change and extend diagnoses based on behavioral measures. The results can already be used in clinical practice to help physicians, patients, and families develop a more detailed and accurate assessments, and provide hope for further technical and methodological improvements through future research.

The previous chapters should help to show the high quality of the nominated projects, and thus the jury had a very difficult task.