Spencer L. BeMent

Bio: Spencer L. BeMent is an academic researcher from University of Michigan. The author has contributed to research in topics: Signal-to-noise ratio (imaging) & Long-term potentiation. The author has an hindex of 14, co-authored 29 publications receiving 1577 citations. Previous affiliations of Spencer L. BeMent include University of Vermont & University of Illinois at Urbana–Champaign.

Performance of planar multisite microprobes in recording extracellular single-unit intracortical activity

Abstract: The microprobes consist of a thin-film electrode array supported by a silicon micromachined substrate and insulated using deposited dielectrics. Microprobes with multiple recording sites spaced from 30 mu m to 200 mu m apart are used to record spontaneous single-unit activity from rat cerebral cortex. Additionally, a theoretical model is used to establish a basis for interpreting the multisite single-unit data. The results suggest that the microprobes (1) couple tightly to the neural tissue with relatively little disturbance to the neural system, (2) facilitate the identification of single units in multiunit records through the use of spatially-separate recording sites, and (3) can be used to detect the cell position in tissue and observe events such as the propagation of electrical activity from the soma to the dendritic tree. >

A direct brain interface based on event-related potentials

Abstract: Cross-correlation between a trigger-averaged event-related potential (ERP) template and continuous electrocorticogram was used to detect movement-related ERP's. The accuracy of ERP detection for the five best subjects (of 17 studied), had hit percentages >90% and false positive percentages <10%. These cases were considered appropriate for operation of a direct brain interface.

Solid-State Electrodes for Multichannel Multiplexed Intracortical Neuronal Recording

Abstract: Thin-film arrays of extracellular recording electrodes have been developed for use in studies of information processing in neural structures and eventual use in closed-loop control of neural prostheses. These probes consist of a silicon substrate which supports an array of thin-film conductors. The conductors are insulated above and below with deposited dielectrics. The electrode sites are defined by openings in the upper dielectric layer and are inlaid with gold to form low-impedance recording surfaces. The probes are typically 15 pim in thickness with shank widths as narrow as 20 ?m. The probe fabrication process is compatible with the inclusion of signal processing circuitry directly on the probe substrate. A 12 channel on-chip signal processor design with per-channel gain of 100, bandwidth of 100 Hz-6 kHz, multiplexed output, and recording-site impedance check capability is described. The probes have adequate strength to penetrate the gerbil pia-arachnoid layer and have recorded single neuron activity of over 500 ?V peak-to-peak from tip, side, and mid-carrier sites. Signal-to-noise ratios as high as 10:1 have been achieved. An equivalent circuit model for the conducting leads, the recording site, and the electrode-electrolyte interface is described. Development of biocompatible insulation and encapsulation materials for long-term implantation of active probes is underway.

BCI2000: a general-purpose brain-computer interface (BCI) system

Abstract: Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.

Brain-computer interfaces for communication and control

Abstract: The brain's electrical signals enable people without muscle control to physically interact with the world.

Brain-computer interface technology: a review of the first international meeting

Abstract: Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.