Carneyross3888

BCI (brain-computer interface) and functional electrical stimulation (FES) technologies have advanced significantly over the last several decades. Recent efforts have involved the integration of these technologies with the goal of restoring functional movement in paralyzed patients. Implantable BCIs have provided neural recordings with increased spatial resolution and have been combined with sophisticated neural decoding algorithms and increasingly capable FES systems to advance efforts toward this goal. This chapter reviews historical developments that have occurred as the exciting fields of BCI and FES have evolved and now overlapped to allow new breakthroughs in medicine, targeting restoration of movement and lost function in users with disabilities. © 2020 Elsevier B.V. All rights reserved.Brain-computer interfaces (BCIs) based on functional magnetic resonance imaging (fMRI) provide an important complement to other noninvasive BCIs. While fMRI has several disadvantages (being nonportable, methodologically challenging, costly, and noisy), it is the only method providing high spatial resolution whole-brain coverage of brain activation. These properties allow relating mental activities to specific brain regions and networks providing a transparent scheme for BCI users to encode information and for real-time fMRI BCI systems to decode the intents of the user. Various mental activities have been used successfully in fMRI BCIs so far that can be classified into the four categories (a) higher-order cognitive tasks (e.g., mental calculation), (b) covert language-related tasks (e.g., mental speech and mental singing), (c) imagery tasks (motor, visual, auditory, tactile, and emotion imagery), and (d) selective attention tasks (visual, auditory, and tactile attention). While the ultimate spatial and temporal resolution of fMRI BCIs is limited by the physiologic properties of the hemodynamic response, technical and analytical advances will likely lead to substantially improved fMRI BCIs in the future using, for example, decoding of imagined letter shapes at 7T as the basis for more "natural" communication BCIs. © 2020 Elsevier B.V. All rights reserved.The gold standard in brain-computer interface (BCI) modalities is multi single-unit recordings in the primary motor cortex. It yields the fastest and most elegant control (i.e., most degrees of freedom and bitrate). Unfortunately, single-unit electrodes are prone to encapsulation, which limit their single-unit recording life. However, encapsulation does not significantly affect intracortical local field potentials (LFPs). LFPs and single-unit activity were recorded from the motor cortices of three monkeys (Macaca fascicularis) while they performed a standard 3D center-out reaching task and a 3D circle-drawing task. The high frequency (HF) (60-200 Hz) spectral amplitudes of a subset of the LFPs were found to be directionally tuned much like single units. In fact, stable isolation of single units on the same electrode increased the likelihood that the HF-LFP would be significantly cosine tuned to hand direction. The presence of significantly tuned single units further increased the likelihood of a tuned HF-LFP, suggesting that this band of HF-LFP activity is at least partially generated by local neuronal action potential currents (i.e., single-unit activity). Given that encapsulation makes recording single units over a long period of time difficult, these results suggest that HF-LFPs may be a more stable and efficient method of monitoring neural activity for BCI applications. © 2020 Elsevier B.V. All rights reserved.Intracranial electroencephalography (iEEG) is measured from electrodes placed in or on the brain. These measurements have an excellent signal-to-noise ratio and iEEG signals have often been used to decode brain activity or drive brain-computer interfaces (BCIs). iEEG recordings are typically done for seizure monitoring in epilepsy patients who have these electrodes placed for a clinical purpose to localize both brain regions that are essential for function and others where seizures start. Brain regions not involved in epilepsy are thought to function normally and provide a unique opportunity to learn about human neurophysiology. Intracranial electrodes measure the aggregate activity of large neuronal populations and recorded signals contain many features. Different features are extracted by analyzing these signals in the time and frequency domain. The time domain may reveal an evoked potential at a particular time after the onset of an event. Decomposition into the frequency domain may show narrowband peaks in the spectrum at specific frequencies or broadband signal changes that span a wide range of frequencies. Broadband power increases are generally observed when a brain region is active while most other features are highly specific to brain regions, inputs, and tasks. Here we describe the spatiotemporal dynamics of several iEEG signals that have often been used to decode brain activity and drive BCIs. © 2020 Elsevier B.V. All rights reserved.To treat stroke and, in particular, to alleviate the personal and social burden of stroke survivors is a main challenge for neuroscience research. Advancements in the knowledge of neurobiologic mechanisms subserving stroke-related damage and recovery provide key data to guide clinicians to tailor interventions to specific patient's needs. How does the brain-computer interface (BCI) fit into this scenario? A technique created to allow completely paralyzed individuals to control the environment recently introduced a new line of development to provide a means to possibly control formation and changes in the brain network organization. In a sort of revolution, similar to the change from geocentric to heliocentric planet organization envisioned by Copernicus, we are facing a critical change in BCI research, moving from a brain to computer direction to a computer to brain one. selleck compound This direction change will profoundly open up new avenues for BCI research and clinical applications. In this chapter, we address this change and discuss present and future applications of this new line idea of BCI use in stroke. © 2020 Elsevier B.V. All rights reserved.