The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Fazli, Siamac; Sannelli, Claudia; Haufe, Stefan; Maeder, Cecilia; Ramsey, Lenny; Sturm, Irene; Curio, Gabriel; Müller, Klaus Robert

doi:10.3389/fnins.2010.00198

Cited by 304 publications

(193 citation statements)

References 140 publications

(158 reference statements)

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“…Perturbations in these signals are correlated with cognitive processes including those involved with attention, emotion and decisionmaking. There is growing interest in the potential of these signals to be used for non-medical applications [2] for instance in applications involving performance monitoring in users. A KT88-1016 EEG system was used with electrodes placed at scalp locations Fz, Cz, Pz, Oz, C3, C4, P3, P4 corresponding to the 10-20 electrode placement system.…”

Section: Eeg Analysismentioning

confidence: 99%

How interaction methods affect image segmentation: User experience in the task

Hebbalaguppe¹,

McGuinness²,

Kuklyte³

et al. 2013

2013 1st IEEE Workshop on User-Centered Computer Vision (UCCV)

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Interactive image segmentation is extensively used in photo editing when the aim is to separate a foreground object from its background so that it is available for various applications. The goal of the interaction is to get an accurate segmentation of the object with the minimal amount of human effort. To improve the usability and user experience using interactive image segmentation we present three interaction methods and study the effect of each using both objective and subjective metrics, such as, accuracy, amount of effort needed, cognitive load and preference of interaction method as voted by users. The novelty of this paper is twofold. First , the evaluation of interaction methods is carried out with objective metrics such as object and boundary accuracies in tandem with subjective metrics to cross check if they support each other. Second, we analyze Electroencephalography (EEG) data obtained from subjects performing the segmentation as an indicator of brain activity. The experimental results potentially give valuable cues for the development of easy-to-use yet efficient interaction methods for image segmentation.

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Section: Eeg Analysismentioning

confidence: 99%

How interaction methods affect image segmentation: User experience in the task

Hebbalaguppe¹,

McGuinness²,

Kuklyte³

et al. 2013

2013 1st IEEE Workshop on User-Centered Computer Vision (UCCV)

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“…They are particularly focused on managing photos, video, web surfing, and music, and although in their infancy, researchers have used a web browser interface to control Google Earth via BCI. 32 BCI systems have progressed beyond the paradigm of improving communications for the disabled. They now offer measurement devices capable of assessing and decoding more brain-states in real-time, allowing for seamless measurement of workload and performance.…”

Section: Current Bci Applicationsmentioning

confidence: 99%

“…point out that another major obstacle to advancing nonmedical application of BCI is the ability to detect accurate intentions between user and computer to control applications. 30 One area showing improvement with EEG technology is with newer electrodes, and research on "dry" electrodes that are easier to use and less time consuming to set-up is underway. There is continued development on electrodes with improved materials, better conduction, and smaller components.…”

Section: Current Bci Applicationsmentioning

confidence: 99%

The Brain Computer Interface Future: Time for a Strategy

Moore¹

2013

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Brain-computer interface (BCI) technology, a subset of human-machine interface, will impose significant utility, relevance, and strategic advantage with national security opportunities and challenges. This technology will advance computing speed, cognitive decision-making, information exchange, and enhanced computational power, resulting in substantially enhanced human performance. A direct connection between the brain and a computer will bypass peripheral nerves and muscles, allowing the brain to have direct control over software and external devices. The military applications for communications, command, control, remote sensors, and weapon deployment with BCI will be significant. Currently this technology is confined to the rehabilitative medicine community for persons with disabilities and the computer gaming industry; however, China, India, Iran, Russia, Japan and European nations are actively working to improve existing electroencephalography, magnetic resonance, functional infrared, and the magnetic encephalography spectrums to develop future military applications. Similar to the experience with computer and networking technology, rapid advancements in neurotechnology will render BCI regulation increasingly challenging. This potential vulnerability will place our infrastructure and individual persons at high risk. The ability to penetrate human brains through BCI will add a new dimension to physical and cyber security. Additionally, moral, ethical, and legal issues will challenge BCI application and employment in the United States. Finally, by 2032 this technology has the potential to revolutionize military dominance much the same way nuclear weapons have done. To maintain the competitive advantage with competitor nations, it is time develop a strategy for the United States and the Air Force to lead in research, design, manufacturing, employment, exploitation, security and counterproliferation of this technology.1

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“…level of attention, tiredness, arousal), interest or endogenous context update as response to a novel and relevant sensorial information. Some examples of application of BCI-WBAN [21] could be the online measure of cognitive load in dangerous works, a communication speller for people suffering from locked-in syndrome or ALS [22], or as an attention/interest detector with potential application in neuromarketing or Ambient Assisted Living (AAL).…”

Section: Bci-wban Definitionmentioning

confidence: 99%

Brain-Computer Interface as Networking Entity in Body Area Networks

López-Gordo

Valle

2015

Lecture Notes in Computer Science

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Abstract.Modern BCIs are non-invasive, wireless, wearable and EEG-based systems capable to transduce brain signals into cognitive information and stream it out in form of data. BCIs have rarely been considered as part of the network. Thus, data communication approaches have not even tried. The consequence is a suboptimal communication in terms of performance, usability and reliability. In this paper we present BCI as a paradigm in Wireless Body Area Networks (WBANs) that interfaces multisensorial sources with the user brain. The BCI-WBAN concept treats cognitive processes as communication end-points in which sensorial information is consumed by cognitive processes. In the context of WBAN, BCIs implement the functionalities of the lower layers of communication open system: signal transduction into data, access control to the physical and cognitive media and data transmission. The BCI-WBAN paradigm extends the scope of WBAN from mere biosignal sensing by adding interpretation of neural correlates of cognitive information.

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The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Cited by 304 publications

References 140 publications

How interaction methods affect image segmentation: User experience in the task

How interaction methods affect image segmentation: User experience in the task

The Brain Computer Interface Future: Time for a Strategy

Brain-Computer Interface as Networking Entity in Body Area Networks

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