Pharmacology

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under he primary entry in the Author Index.

Ramoser H., Muller-Gerking J., Pfurtscheller G.

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. The authors demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.

Robinson C.J.

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under he primary entry in the Author Index.

Hastings J.A., Vannah W.M., Drvaric D.M.

This report characterizes the performance of three digital filters, when applied to residual limb shape maps. The three filters mere an averaging filter, a uniform window Fourier filter, and a Hamming window Fourier filter. The frequency responses of the three filters were calculated from theory, and experimentally observed. Experimental observations consisted of responses on single-frequency lobed shapes, and on residual limb shapes. Seven trans-tibial limb molds were digitized, three times each. Each resulting shape was then passed through each of the three filters. The before and after shapes were then compared. A Hamming window filter (low-pass frequency of 10 cycles per revolution, 24 coefficients, Hamming number of 0.25) achieved the best performance based on maintained amount of residual limb frequencies, and on visual observation.

Robinson C.J.

Wolpaw J.R., Birbaumer N., Heetderks W.J., McFarland D.J., Peckham P.H., Schalk G., Donchin E., Quatrano L.A., Robinson C.J., Vaughan T.M.

Scott T.R., Heasman J.M., Vare V.A., Flynn R.Y., Gschwind C.R., Middleton J.W., Rutkowski S.B.

Electrical stimulation has been applied to the paralyzed muscles of both hands of two persons with tetraplegia using percutneous acid implantable electrodes. Two separate methods of user control were being investigated. The first monitored the myoelectric signals from the user's own sternocleidomastoid muscles and the second monitored wrist joint angle. These signals were used as commands to modify the stimulated grasps. The hands were instrumented to detect the degree of hand closure and grip force and the users matched these to specific target parameters using the controller during tracking tasks. Performance in these tracking tasks was measured quantitatively. Wrist control was found to be less sensitive to the direction of hand opening/closing required than the myoelectric control. The user's performance with the myoelectric control demonstrated sensitivity to the target size and the speed of hand movement in response to the command control. The wrist controller required less training than the myoelectric controller for users to become proficient in its use. Based on these results, the wrist controller and the myoelectric controller both provide successful control of bilateral hand grasp and release. Of the two controllers, the wrist controller is likely to provide the greater ease of use, although it is only available to the population of users with active wrist extension.

Middendorf M., McMillan G., Calhoun G., Jones K.S.

The Air Force Research Laboratory has implemented and evaluated two brain-computer interfaces (BCI's) that translate the steady-state visual evoked response into a control signal for operating a physical device or computer program. In one approach, operators self-regulate the brain response; the other approach uses multiple evoked responses.

Wolpaw J.R., McFarland D.J., Vaughan T.M.

Studies at the Wadsworth Center over the past 14 years have shown that people with or without motor disabilities can learn to control the amplitude of /spl mu/ or /spl beta/ rhythms in electroencephalographic (EEG) activity recorded from the scalp over the sensorimotor cortex and can use that control to move a cursor on a computer screen in one or two dimensions. This EEG-based brain-computer interface (BCI) could provide a new augmentative communication technology for those who are totally paralyzed or have other severe motor impairments, Present research focuses on improving the speed and accuracy of BCI communication.

Birbaumer N., Kubler A., Ghanayim N., Hinterberger T., Perelmouter J., Kaiser J., Iversen I., Kotchoubey B., Neumann N., Flor H.

The thought translation device trains locked-in patients to self regulate slow cortical potentials (SCP's) of their electroencephalogram (EEG). After operant learning of SCP self control, patients select letters, words or pictograms in a computerized language support program. Results of five respirated, locked-in-patients are described, demonstrating the usefulness of the thought translation device as an alternative communication channel in motivated totally paralyzed patients with amyotrophic lateral sclerosis.

Makeig S., Enghoff S., Tzyy-Ping Jung, Sejnowski T.J.

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central /spl mu/-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to he a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central /spl mu/ activities. The authors demonstrate using data from a visual selective attention task that ICA-derived /spl mu/-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels, ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain actuated control in motor-limited and locked-in subjects.

Babiloni F., Cincotti F., Lazzarini L., Millan J., Mourino J., Varsta M., Heikkonen J., Bianchi L., Marciani M.G.

Electroencephalograph (EEG)-based brain-computer interfaces (BCI's) require on-line detection of mental states from spontaneous EEG signals, In this framework, surface Laplacian (SL) transformation of EEG signals has proved to improve the recognition scores of imagined motor activity. The results the authors obtained in the first year of an European project named adaptive brain interfaces (ABI) suggest that: (1) the detection of mental imagined activity can be obtained by using the signal space projection (SSP) method as a classifier and (2) a particular type of electrodes can be used in such a BCI device, reconciling the benefits of SL waveforms and the need for the use of few electrodes. Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements.

Penny W.D., Roberts S.J., Curran E.A., Stokes M.J.

Presents an overview of the authors' research into brain-computer interfacing (BCI). This comprises an offline study of the effect of motor imagery on EEG and an online study that uses pattern classifiers incorporating parameter uncertainty and temporal information to discriminate between different cognitive tasks in real-time.

Birch G.E., Mason S.G.

The ultimate goal of the authors' research is to utilize voluntary motor-related potentials recorded from the scalp in a direct Brain Computer Interface for asynchronous control applications. This type of interface will allow an individual with a high-level impairment to have effective and sophisticated control of devices such as wheelchairs, robotic assistive appliances, computers, and neural prostheses.