Grand challenges of brain computer interfaces in the years to come

Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs

Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.

Progress in Brain Computer Interface: Challenges and Opportunities

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

The grand challenges of Science Robotics

One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpinning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts that they will bring. Finally, the last challenge is related to responsible innovation and how ethics and security should be carefully considered as we develop the technology further.

Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model

How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600–1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain–machine interfaces exploiting population signals.

Nanostructures: a platform for brain repair and augmentation

Nanoscale structures have been at the core of research efforts dealing with integration of nanotechnology into novel electronic devices for the last decade. Because the size of nanomaterials is of the same order of magnitude as biomolecules, these materials are valuable tools for nanoscale manipulation in a broad range of neurobiological systems. For instance, the unique electrical and optical properties of nanowires, nanotubes, and nanocables with vertical orientation, assembled in nanoscale arrays, have been used in many device applications such as sensors that hold the potential to augment brain functions. However, the challenge in creating nanowires/nanotubes or nanocables array-based sensors lies in making individual electrical connections fitting both the features of the brain and of the nanostructures. This review discusses two of the most important applications of nanostructures in neuroscience. First, the current approaches to create nanowires and nanocable structures are reviewed to critically evaluate their potential for developing unique nanostructure based sensors to improve recording and device performance to reduce noise and the detrimental effect of the interface on the tissue. Second, the implementation of nanomaterials in neurobiological and medical applications will be considered from the brain augmentation perspective. Novel applications for diagnosis and treatment of brain diseases such as multiple sclerosis, meningitis, stroke, epilepsy, Alzheimer's disease, schizophrenia, and autism will be considered. Because the blood brain barrier (BBB) has a defensive mechanism in preventing nanomaterials arrival to the brain, various strategies to help them to pass through the BBB will be discussed. Finally, the implementation of nanomaterials in neurobiological applications is addressed from the brain repair/augmentation perspective. These nanostructures at the interface between nanotechnology and neuroscience will play a pivotal role not only in addressing the multitude of brain disorders but also to repair or augment brain functions.

Biofuel cell based on microscale nanostructured electrodes with inductive coupling to rat brain neurons

Miniature, self-contained biodevices powered by biofuel cells may enable a new generation of implantable, wireless, minimally invasive neural interfaces for neurophysiological in vivo studies and for clinical applications. Here we report on the fabrication of a direct electron transfer based glucose/oxygen enzymatic fuel cell (EFC) from genuinely three-dimensional (3D) nanostructured microscale gold electrodes, modified with suitable biocatalysts. We show that the process underlying the simple fabrication method of 3D nanostructured electrodes is based on an electrochemically driven transformation of physically deposited gold nanoparticles. We experimentally demonstrate that mediator-, cofactor-, and membrane-less EFCs do operate in cerebrospinal fluid and in the brain of a rat, producing amounts of electrical power sufficient to drive a self-contained biodevice, viz. 7 μW cm⁻²in vitro and 2 μW cm⁻²in vivo at an operating voltage of 0.4 V. Last but not least, we also demonstrate an inductive coupling between 3D nanobioelectrodes and living neurons.

Real-time fMRI neurofeedback: progress and challenges

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

Nanowire-based electrode for acute in vivo neural recordings in the brain

We present an electrode, based on structurally controlled nanowires, as a first step towards developing a useful nanostructured device for neurophysiological measurements in vivo. The sensing part of the electrode is made of a metal film deposited on top of an array of epitaxially grown gallium phosphide nanowires. We achieved the first functional testing of the nanowire-based electrode by performing acute in vivo recordings in the rat cerebral cortex and withstanding multiple brain implantations. Due to the controllable geometry of the nanowires, this type of electrode can be used as a model system for further analysis of the functional properties of nanostructured neuronal interfaces in vivo.

A low-cost, portable, micro-controlled device for multi-channel LED visual stimulation

Light emitting diodes (LEDs) are extensively used as light sources to investigate visual and visually related function and dysfunction. Here, we describe the design of a compact, low-cost, stand-alone LED-based system that enables the configuration, storage and presentation of elaborate visual stimulation paradigms. The core functionality of this system is provided by a microcontroller whose ultra-low power consumption makes it well suited for long lasting battery applications. The effective use of hardware resources is managed by multi-layered architecture software that provides an intuitive and user-friendly interface. In the configuration mode, different stimulation sequences can be created and memorized for ten channels, independently. LED-driving current output can be set either as continuous or pulse modulated, up to 500 Hz, by duty cycle adjustments. In run mode, multiple-channel stimulus sequences are automatically applied according to the pre-programmed protocol. Steady state visual evoked potentials were successfully recorded in five subjects with no visible electromagnetic interferences from the stimulator, demonstrating the efficacy of combining our prototyped equipment with electrophysiological techniques. Finally, we discuss a number of possible improvements for future development of our project.

The hybrid BCI

Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a “brain switch”. For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system.