Spencer Kellis

Decoding Motor Imagery from the Posterior Parietal Cortex of a Tetraplegic Human

Brain imagination to control external devices Studies in monkeys have implicated the brains posterior parietal cortex in high-level coding of planned and imagined actions. Aflalo et al. implanted two microelectrode arrays in the posterior parietal cortex of a tetraplegic patient (see the Perspective by Pruszynski and Diedrichsen). They asked the patient to imagine various types of limb or eye movements. As predicted, motor imagery involved the same types of neural population activity involved in actual movements, which could potentially be exploited in prosthetic limb control. Science, this issue p. 906; see also p. 860 Neurons in the human posterior parietal cortex encode high-level aspects of imagined movements. [Also see Perspective by Pruszynski and Diedrichsen] Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.

Human neocortical electrical activity recorded on nonpenetrating microwire arrays: applicability for neuroprostheses.

OBJECT The goal of this study was to determine whether a nonpenetrating, high-density microwire array could provide sufficient information to serve as the interface for decoding motor cortical signals. METHODS Arrays of nonpenetrating microwires were implanted over the human motor cortex in 2 patients. The patients performed directed stereotypical reaching movements in 2 directions. The resulting data were used to determine whether the reach direction could be distinguished through a frequency power analysis. RESULTS Correlation analysis revealed decreasing signal correlation with distance. The gamma-band power during motor planning allowed binary classification of gross directionality in the reaching movements. The degree of power change was correlated to the underlying gyral pattern. CONCLUSIONS The nonpenetrating microwire platform showed good potential for allowing differentiated signals to be recorded with high spatial fidelity without cortical penetration.

Electronic color charts for dielectric films on silicon

This paper presents the calculation of the perceived color of dielectric films on silicon. A procedure is shown for computing the perceived color for an arbitrary light source, light incident angle, and film thickness. The calculated color is converted into RGB parameters that can be displayed on a color monitor, resulting in the generation of electronic color charts for dielectric films. This paper shows generated electronic color charts for both silicon dioxide and silicon nitride films on silicon.

Toward More Versatile and Intuitive Cortical Brain–Machine Interfaces

Brain-machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain-machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics.

The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures

The extensive distribution and simultaneous termination of seizures across cortical areas has led to the hypothesis that seizures are caused by large-scale coordinated networks spanning these areas. This view, however, is difficult to reconcile with most proposed mechanisms of seizure spread and termination, which operate on a cellular scale. We hypothesize that seizures evolve into self-organized structures wherein a small seizing territory projects high-intensity electrical signals over a broad cortical area. Here we investigate human seizures on both small and large electrophysiological scales. We show that the migrating edge of the seizing territory is the source of travelling waves of synaptic activity into adjacent cortical areas. As the seizure progresses, slow dynamics in induced activity from these waves indicate a weakening and eventual failure of their source. These observations support a parsimonious theory for how large-scale evolution and termination of seizures are driven from a small, migrating cortical area.

Hand Shape Representations in the Human Posterior Parietal Cortex

Humans shape their hands to grasp, manipulate objects, and to communicate. From nonhuman primate studies, we know that visual and motor properties for grasps can be derived from cells in the posterior parietal cortex (PPC). Are non-grasp-related hand shapes in humans represented similarly? Here we show for the first time how single neurons in the PPC of humans are selective for particular imagined hand shapes independent of graspable objects. We find that motor imagery to shape the hand can be successfully decoded from the PPC by implementing a version of the popular Rock-Paper-Scissors game and its extension Rock-Paper-Scissors-Lizard-Spock. By simultaneous presentation of visual and auditory cues, we can discriminate motor imagery from visual information and show differences in auditory and visual information processing in the PPC. These results also demonstrate that neural signals from human PPC can be used to drive a dexterous cortical neuroprosthesis. SIGNIFICANCE STATEMENT This study shows for the first time hand-shape decoding from human PPC. Unlike nonhuman primate studies in which the visual stimuli are the objects to be grasped, the visually cued hand shapes that we use are independent of the stimuli. Furthermore, we can show that distinct neuronal populations are activated for the visual cue and the imagined hand shape. Additionally we found that auditory and visual stimuli that cue the same hand shape are processed differently in PPC. Early on in a trial, only the visual stimuli and not the auditory stimuli can be decoded. During the later stages of a trial, the motor imagery for a particular hand shape can be decoded for both modalities.

Multi-scale analysis of neural activity in humans: Implications for micro-scale electrocorticography.

OBJECTIVE Electrocorticography grids have been used to study and diagnose neural pathophysiology for over 50 years, and recently have been used for various neural prosthetic applications. Here we provide evidence that micro-scale electrodes are better suited for studying cortical pathology and function, and for implementing neural prostheses. METHODS This work compares dynamics in space, time, and frequency of cortical field potentials recorded by three types of electrodes: electrocorticographic (ECoG) electrodes, non-penetrating micro-ECoG (μECoG) electrodes that use microelectrodes and have tighter interelectrode spacing; and penetrating microelectrodes (MEA) that penetrate the cortex to record single- or multiunit activity (SUA or MUA) and local field potentials (LFP). RESULTS While the finest spatial scales are found in LFPs recorded intracortically, we found that LFP recorded from μECoG electrodes demonstrate scales of linear similarity (i.e., correlation, coherence, and phase) closer to the intracortical electrodes than the clinical ECoG electrodes. CONCLUSIONS We conclude that LFPs can be recorded intracortically and epicortically at finer scales than clinical ECoG electrodes are capable of capturing. SIGNIFICANCE Recorded with appropriately scaled electrodes and grids, field potentials expose a more detailed representation of cortical network activity, enabling advanced analyses of cortical pathology and demanding applications such as brain-computer interfaces.

TRaX: A Multi-Threaded Architecture for Real-Time Ray Tracing

Ray tracing is a technique used for generating highly realistic computer graphics images. In this paper, we explore the design of a simple but extremely parallel, multi-threaded, multi-core processor architecture that performs real-time ray tracing. Our architecture, called TRaX for Threaded Ray eXecution, consists of a set of thread states that include commonly used functional units for each thread and share large functional units through a programmable interconnect to maximize utilization. The memory system takes advantage of the applications read-only access to the scene database and write-only access to the frame buffer output to provide efficient data delivery with a relatively simple structure. Preliminary results indicate that a multi-core version of the architecture running at a modest speed of 500 MHz already provides real-time ray traced images for scenes of a complexity found in video games. We also explore the architectural impact of a ray tracer that uses procedural (computed) textures rather than image-based (look-up) textures to trade computation for reduced memory bandwidth.

Recording and Decoding for Neural Prostheses

This paper reviews technologies and signal processing algorithms for decoding peripheral nerve and electrocorticogram signals to interpret human intent and control prosthetic arms. The review includes a discussion of human motor system physiology and physiological signals that can be used to decode motor intent, electrode technology for acquiring neural data, and signal processing methods including decoders based on Kalman filtering and least-squares regressors. Representative results from human experiments demonstrate the progress that has been made in neural decoding and its potential for developing neuroprosthetic arms that act and feel like natural arms.

Potential for unreliable interpretation of EEG recorded with microelectrodes

Recent studies in epilepsy, cognition, and brain machine interfaces have shown the utility of recording intracranial electroencephalography (iEEG) with greater spatial resolution. Many of these studies utilize microelectrodes connected to specialized amplifiers that are optimized for such recordings. We recently measured the impedances of several commercial microelectrodes and demonstrated that they will distort iEEG signals if connected to clinical EEG amplifiers commonly used in most centers. In this study we demonstrate the clinical implications of this effect and identify some of the potential difficulties in using microelectrodes.