Brain-computer interfaces for hci and games

We study the research themes and the state-of-the-art of brain-computer interaction. Brain-computer interface research has seen much progress in the medical domain, for example for prosthesis control or as biofeedback therapy for the treatment of neurological disorders. Here, however, we look at brain-computer interaction especially as it applies to research in Human-Computer Interaction (HCI). Through this workshop and continuing discussions, we aim to define research approaches and applications that apply to disabled and able-bodied users across a variety of real-world usage scenarios. Entertainment and game design is one of the application areas that will be considered

Abiner, Abinericus, Abinnericus

Interpretación de las estampillas ibero-romanas sobre mortero de Caminreal como textos no equivalentes entre sí, y de la palabra ibérica abiner como un antropónimo, y no como una traducción del vocablo latino servus

Future Directions in Brain/Neuronal Computer Interaction (Future BNCI)

Brain-Computer Interface (BCI) research has made great progress recently [1-3]. However, this progress has some negative side effects: growing fragmentation among different researchers, confusion about the best research directions, and ongoing disagreement over terms and definitions. Future BNCI is a Coordination and Support Action funded by the European Commission that aims to counteract these trends by helping new and existing researchers identify each other, encouraging effective collaborations, developing roadmaps and frameworks, and establishing standardized terminology. The knowledge developed in Future BNCI will be disseminated through conferences, workshops, journal publications, a book, and a website

Conditions Necessary for the Transfer of Antimicrobial Resistance in Poultry Litter

Animal manures contain a large and diverse reservoir of antimicrobial resistance (AMR) genes that could potentially spillover into the general population through transfer of AMR to antibiotic-susceptible pathogens. The ability of poultry litter microbiota to transmit AMR was examined in this study. Abundance of phenotypic AMR was assessed for litter microbiota to the antibiotics: ampicillin (Ap; 25 μg/mL), chloramphenicol (Cm; 25 μg/mL), streptomycin (Sm; 100 μg/mL), and tetracycline (Tc; 25 μg/mL). qPCR was used to estimate gene load of streptomycin-resistance and sulfonamide-resistance genes aadA1 and sul1, respectively, in the poultry litter community. AMR gene load was determined relative to total bacterial abundance using 16S rRNA qPCR. Poultry litter contained 108 CFU/g, with Gram-negative enterics representing a minor population (4 CFU/g). There was high abundance of resistance to Sm (106 to 107 CFU/g) and Tc (106 to 107 CFU/g) and a sizeable antimicrobial-resistance gene load in regards to gene copies per bacterial genome (aadA1: 0.0001–0.0060 and sul1: 0.0355–0.2455). While plasmid transfer was observed from Escherichia coli R100, as an F-plasmid donor control, to the Salmonella recipient in vitro, no AMR Salmonella were detected in a poultry litter microcosm with the inclusion of E. coli R100. Confirmatory experiments showed that isolated poultry litter bacteria were not interfering with plasmid transfer in filter matings. As no R100 transfer was observed at 25 °C, conjugative plasmid pRSA was chosen for its high plasmid transfer frequency (10−4 to 10−5) at 25 °C. While E. coli strain background influenced the persistence of pRSA in poultry litter, no plasmid transfer to Salmonella was ever observed. Although poultry litter microbiota contains a significant AMR gene load, potential to transmit resistance is low under conditions commonly used to assess plasmid conjugation

Affective Brain-Computer Interfaces (aBCI 2011)

Recently, many groups (see Zander and Kothe. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J. Neural Eng., 8, 2011) have worked toward expanding brain-computer interface (BCI) systems to include not only active control, but also passive mental state monitoring to enhance human computer interaction (HCI). Many studies have shown that brain imaging technologies can reveal information about the affective and cognitive state of a subject, and that the interaction between humans and machines can be aided by the recognition of those user states. New developments including practical sensors, new machine learning software, and improved interaction with the HCI community are leading us to systems that seamlessly integrate passively recorded information to improve interactions with the outside world. To achieve robust passive BCIs, efforts from applied and basic sciences have to be combined. On the one hand, applied fields such as affective computing aim to develop applications that adapt to changes in the user states and thereby enrich interaction, leading to a more natural and effective usability. On the other hand, basic research in neuroscience advances our understanding of the neural processes associated with emotions. Similar advancements are made for more cognitive mental states such as attention, workload, or fatigue

Brain-Computer Interfacing for Intelligent Systems

Advances in cognitive neuroscience and brain-imaging technologies give us the unprecedented ability to interface directly with brain activity. These technologies let us monitor physical processes in the brain that correspond with certain forms of thought. Researchers have begun using these technologies to build brain-computer interfaces (BCIs)—communication systems that don't depend on the brain's normal output pathways of peripheral nerves and muscles. Four short articles provide a quick overview of the past, present, and future of BCIs