Brain-computer interfaces for communication and control.

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.

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

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

Brain-computer interfaces for communication and control

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.

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

https://www.lehigh.edu/~inbios21/PDF/Fall2007/Brain-Machine_Interface.pdf

Brain–machine interfaces: past, present and future

The field topography of ERG components in man—I. The photopic luminance response

The brain response interface: communication through visually-induced electrical brain responses

The topography of visual evoked response properties across the visual field

PURPOSE To investigate focal abnormalities in the electroretinogram (ERG) signal in diabetic patients, with and without retinopathy, using a multifocal ERG METHODS Sixteen patients with diabetes mellitus, 8 of whom had diabetic retinopathy (mean duration of diabetes: 185 years) and 19 approximately age-matched healthy volunteers underwent multifocal ERG testing One hundred three retinal locations within the central 50 degrees were stimulated concurrently, according to a pseudorandom m-sequence Response components were extracted for each stimulated retinal location RESULTS In diabetic patients with retinopathy, the overall amplitudes were reduced (P < 001), and peak implicit times were increased (P < 001) in the first-order component (mean flash response) and in the first slice of the second-order component (local two flash interaction) In addition, local reductions of amplitude could be seen in the first- and second-order components In patients without retinopathy, only amplitudes of the second-order component were reduced (P < 001) Another salient difference was observed in a special feature of the second-order component that was reduced in diabetic patients, with and without retinopathy (P < 005) CONCLUSIONS Second-order components depend on nonlinear dynamics Thus our findings indicate changes in the nonlinear dynamics of a fast-gain control in diabetic patients, presumably located in the inner retina This suggests that early functional changes of the inner retina are evident in diabetic patients before impairment of the outer retina is observed Multifocal nonlinear analysis permits the detection of subclinical diabetic retinopathy and offers the advantage of topographic mapping of retinal dysfunction

Mapping of retinal function in diabetic retinopathy using the multifocal electroretinogram.

An algorithm is presented for the fast computation of the m-transform, a Hadamard transform intimately related to cross-correlation of analog signals with binary m-sequences. It is shown that m-transforms are in the same Hadamard equivalence class as Walsh–Hadamard transforms and can, thus, becomputed by means of the Fast Walsh Transform (FWT) algorithm, preceded and followed by a permutation. The FWT is performed in place in the original data array, while the permutations are executed during loading and reading of this array. Real-time generation of the array addresses for loading and reading adds little to execution time of the FWT. The implementation described here lends itself particularly well to applications in linear and nonlinear systems analysis.

Erich E. Sutter

Papers

The field topography of ERG components in man—I. The photopic luminance response

The brain response interface: communication through visually-induced electrical brain responses

The topography of visual evoked response properties across the visual field

Mapping of retinal function in diabetic retinopathy using the multifocal electroretinogram.

The fast m-transform: a fast computation of cross-correlations with binary m-sequences