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Berlin Brain-Computer Interface : The HCl communication channel for discovery

Identifieur interne : 000937 ( PascalFrancis/Curation ); précédent : 000936; suivant : 000938

Berlin Brain-Computer Interface : The HCl communication channel for discovery

Auteurs : Roman Krepki [Allemagne] ; Gabriel Curio [Allemagne] ; Benjamin Blankertz [Allemagne] ; Klaus-Robert Müller [Allemagne]

Source :

RBID : Pascal:07-0221497

Descripteurs français

English descriptors

Abstract

The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain-Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies.
pA  
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A03   1    @0 Int .j. hum.-comput. stud.
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A08 01  1  ENG  @1 Berlin Brain-Computer Interface : The HCl communication channel for discovery
A09 01  1  ENG  @1 Ambient intelligence
A11 01  1    @1 KREPKI (Roman)
A11 02  1    @1 CURIO (Gabriel)
A11 03  1    @1 BLANKERTZ (Benjamin)
A11 04  1    @1 MÜLLER (Klaus-Robert)
A12 01  1    @1 CAI (Yang) @9 ed.
A14 01      @1 Research Group for Intelligent Data Analysis (IDA), Fraunhofer Institute for Computer Architecture and Software Technology (FhG-FIRST), Kekuléstr, 7 @2 12489 Berlin @3 DEU @Z 1 aut. @Z 3 aut. @Z 4 aut.
A14 02      @1 Department of Neurology, Neurophysics Group, Free University of Berlin, Campus Benjamin Franklin, Charité-University Medicine, Hindenburgdamm 30 @2 12200 Berlin @3 DEU @Z 2 aut.
A14 03      @1 Faculty for Computer Science, Department for Neuroinformatics, University of Potsdam, August-Bebel-Street, 89 @2 14482 Potsdam @3 DEU @Z 4 aut.
A15 01      @1 Carnegie Mellon University, 4720 Forbes Avenue, CIC room 2218 @2 Pittsburgh, PA 15213 @3 USA @Z 1 aut.
A18 01  1    @1 Association for Computing Machinery. Special Interest Group on Computer-Human Interaction (SIGCHI) @2 New York @3 USA @9 org-cong.
A20       @1 460-477
A21       @1 2007
A23 01      @0 ENG
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A44       @0 0000 @1 © 2007 INIST-CNRS. All rights reserved.
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A66 01      @0 GBR
C01 01    ENG  @0 The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain-Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies.
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C03 05  X  SPA  @0 Videojuego @5 10
C03 06  X  FRE  @0 Intelligence artificielle @5 11
C03 06  X  ENG  @0 Artificial intelligence @5 11
C03 06  X  SPA  @0 Inteligencia artificial @5 11
C03 07  X  FRE  @0 Système complexe @5 12
C03 07  X  ENG  @0 Complex system @5 12
C03 07  X  SPA  @0 Sistema complejo @5 12
C03 08  X  FRE  @0 Système réparti @5 13
C03 08  X  ENG  @0 Distributed system @5 13
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C03 09  X  SPA  @0 Tratamiento datos @5 14
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C03 11  3  FRE  @0 Vie artificielle @5 16
C03 11  3  ENG  @0 Artificial life @5 16
C03 12  X  FRE  @0 Charge travail @5 17
C03 12  X  ENG  @0 Workload @5 17
C03 12  X  SPA  @0 Carga trabajo @5 17
C03 13  X  FRE  @0 Cerveau @5 18
C03 13  X  ENG  @0 Brain @5 18
C03 13  X  SPA  @0 Cerebro @5 18
C03 14  X  FRE  @0 Moelle épinière @5 19
C03 14  X  ENG  @0 Spinal cord @5 19
C03 14  X  SPA  @0 Médula espinal @5 19
C03 15  X  FRE  @0 Encéphale @5 20
C03 15  X  ENG  @0 Encephalon @5 20
C03 15  X  SPA  @0 Encéfalo @5 20
C03 16  X  FRE  @0 Homme @5 21
C03 16  X  ENG  @0 Human @5 21
C03 16  X  SPA  @0 Hombre @5 21
C03 17  X  FRE  @0 Système nerveux central @5 22
C03 17  X  ENG  @0 Central nervous system @5 22
C03 17  X  SPA  @0 Sistema nervioso central @5 22
C03 18  X  FRE  @0 Electroencéphalographie @5 23
C03 18  X  ENG  @0 Electroencephalography @5 23
C03 18  X  SPA  @0 Electroencefalografía @5 23
C03 19  X  FRE  @0 Modélisation @5 24
C03 19  X  ENG  @0 Modeling @5 24
C03 19  X  SPA  @0 Modelización @5 24
C03 20  3  FRE  @0 Jeu ordinateur @5 25
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C03 21  X  FRE  @0 Algorithme réparti @5 26
C03 21  X  ENG  @0 Distributed algorithm @5 26
C03 21  X  SPA  @0 Algoritmo repartido @5 26
C03 22  X  FRE  @0 Sensibilité tactile @5 27
C03 22  X  ENG  @0 Tactile sensitivity @5 27
C03 22  X  SPA  @0 Sensibilidad tactil @5 27
C03 23  X  FRE  @0 Equipement entrée sortie @5 41
C03 23  X  ENG  @0 Input output equipment @5 41
C03 23  X  SPA  @0 Equipo entrada salida @5 41
N21       @1 155
N44 01      @1 OTO
N82       @1 OTO
pR  
A30 01  1  ENG  @1 Workshop on AmI for Scientific Discovery @3 Vienna AUT @4 2004-04-25

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Pascal:07-0221497

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<div type="abstract" xml:lang="en">The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain-Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies.</div>
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<s9>org-cong.</s9>
</fA18>
<fA20>
<s1>460-477</s1>
</fA20>
<fA21>
<s1>2007</s1>
</fA21>
<fA23 i1="01">
<s0>ENG</s0>
</fA23>
<fA43 i1="01">
<s1>INIST</s1>
<s2>14299</s2>
<s5>354000159380050040</s5>
</fA43>
<fA44>
<s0>0000</s0>
<s1>© 2007 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45>
<s0>1 p.</s0>
</fA45>
<fA47 i1="01" i2="1">
<s0>07-0221497</s0>
</fA47>
<fA60>
<s1>P</s1>
<s2>C</s2>
</fA60>
<fA61>
<s0>A</s0>
</fA61>
<fA64 i1="01" i2="1">
<s0>International journal of human-computer studies</s0>
</fA64>
<fA66 i1="01">
<s0>GBR</s0>
</fA66>
<fC01 i1="01" l="ENG">
<s0>The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain-Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies.</s0>
</fC01>
<fC02 i1="01" i2="X">
<s0>001D02B04</s0>
</fC02>
<fC02 i1="02" i2="X">
<s0>001D02C</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE">
<s0>Interface utilisateur</s0>
<s5>06</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG">
<s0>User interface</s0>
<s5>06</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA">
<s0>Interfase usuario</s0>
<s5>06</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE">
<s0>Application médicale</s0>
<s5>07</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG">
<s0>Medical application</s0>
<s5>07</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA">
<s0>Aplicación medical</s0>
<s5>07</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE">
<s0>Multimédia</s0>
<s5>08</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG">
<s0>Multimedia</s0>
<s5>08</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA">
<s0>Multimedia</s0>
<s5>08</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE">
<s0>Décodage</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG">
<s0>Decoding</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA">
<s0>Desciframiento</s0>
<s5>09</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE">
<s0>Jeu vidéo</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG">
<s0>Video game</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA">
<s0>Videojuego</s0>
<s5>10</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE">
<s0>Intelligence artificielle</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG">
<s0>Artificial intelligence</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA">
<s0>Inteligencia artificial</s0>
<s5>11</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE">
<s0>Système complexe</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG">
<s0>Complex system</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA">
<s0>Sistema complejo</s0>
<s5>12</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE">
<s0>Système réparti</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG">
<s0>Distributed system</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA">
<s0>Sistema repartido</s0>
<s5>13</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE">
<s0>Traitement donnée</s0>
<s5>14</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG">
<s0>Data processing</s0>
<s5>14</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA">
<s0>Tratamiento datos</s0>
<s5>14</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE">
<s0>Contrôle information</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG">
<s0>Information control</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA">
<s0>Control información</s0>
<s5>15</s5>
</fC03>
<fC03 i1="11" i2="3" l="FRE">
<s0>Vie artificielle</s0>
<s5>16</s5>
</fC03>
<fC03 i1="11" i2="3" l="ENG">
<s0>Artificial life</s0>
<s5>16</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE">
<s0>Charge travail</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG">
<s0>Workload</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA">
<s0>Carga trabajo</s0>
<s5>17</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE">
<s0>Cerveau</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG">
<s0>Brain</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA">
<s0>Cerebro</s0>
<s5>18</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE">
<s0>Moelle épinière</s0>
<s5>19</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG">
<s0>Spinal cord</s0>
<s5>19</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA">
<s0>Médula espinal</s0>
<s5>19</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE">
<s0>Encéphale</s0>
<s5>20</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG">
<s0>Encephalon</s0>
<s5>20</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA">
<s0>Encéfalo</s0>
<s5>20</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE">
<s0>Homme</s0>
<s5>21</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG">
<s0>Human</s0>
<s5>21</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA">
<s0>Hombre</s0>
<s5>21</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE">
<s0>Système nerveux central</s0>
<s5>22</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG">
<s0>Central nervous system</s0>
<s5>22</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA">
<s0>Sistema nervioso central</s0>
<s5>22</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE">
<s0>Electroencéphalographie</s0>
<s5>23</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG">
<s0>Electroencephalography</s0>
<s5>23</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA">
<s0>Electroencefalografía</s0>
<s5>23</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE">
<s0>Modélisation</s0>
<s5>24</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG">
<s0>Modeling</s0>
<s5>24</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA">
<s0>Modelización</s0>
<s5>24</s5>
</fC03>
<fC03 i1="20" i2="3" l="FRE">
<s0>Jeu ordinateur</s0>
<s5>25</s5>
</fC03>
<fC03 i1="20" i2="3" l="ENG">
<s0>Computer games</s0>
<s5>25</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE">
<s0>Algorithme réparti</s0>
<s5>26</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG">
<s0>Distributed algorithm</s0>
<s5>26</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA">
<s0>Algoritmo repartido</s0>
<s5>26</s5>
</fC03>
<fC03 i1="22" i2="X" l="FRE">
<s0>Sensibilité tactile</s0>
<s5>27</s5>
</fC03>
<fC03 i1="22" i2="X" l="ENG">
<s0>Tactile sensitivity</s0>
<s5>27</s5>
</fC03>
<fC03 i1="22" i2="X" l="SPA">
<s0>Sensibilidad tactil</s0>
<s5>27</s5>
</fC03>
<fC03 i1="23" i2="X" l="FRE">
<s0>Equipement entrée sortie</s0>
<s5>41</s5>
</fC03>
<fC03 i1="23" i2="X" l="ENG">
<s0>Input output equipment</s0>
<s5>41</s5>
</fC03>
<fC03 i1="23" i2="X" l="SPA">
<s0>Equipo entrada salida</s0>
<s5>41</s5>
</fC03>
<fN21>
<s1>155</s1>
</fN21>
<fN44 i1="01">
<s1>OTO</s1>
</fN44>
<fN82>
<s1>OTO</s1>
</fN82>
</pA>
<pR>
<fA30 i1="01" i2="1" l="ENG">
<s1>Workshop on AmI for Scientific Discovery</s1>
<s3>Vienna AUT</s3>
<s4>2004-04-25</s4>
</fA30>
</pR>
</standard>
</inist>
</record>

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