Curated Papers > Highly Cited Authoritative Literature on "Brain-Computer Interfaces"

Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general.

Literature Information

DOI	10.1088/1741-2560/8/2/025005
PMID	21436512
Journal	Journal of neural engineering
Impact Factor	3.8
JCR Quartile	Q2
Publication Year	2011
Times Cited	189
Keywords	Passive Brain-Computer Interface, Cognitive Monitoring, Real-time Brain Signal Decoding, User Intentions, Emotional States
Literature Type	Journal Article, Review
ISSN	1741-2552
Pages	025005
Issue	8(2)
Authors	Thorsten O Zander, Christian Kothe

TL;DR

This paper introduces a novel approach called passive brain-computer interface (BCI), which integrates cognitive monitoring with real-time brain signal decoding to enhance user interaction by providing insights into intentions, emotional states, and situational interpretations. By categorizing BCI applications and focusing on healthy users, this research highlights the potential of passive BCI in improving communication and control in technical systems based solely on brain activity.

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Passive Brain-Computer Interface · Cognitive Monitoring · Real-time Brain Signal Decoding · User Intentions · Emotional States

Abstract

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

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Primary Questions Addressed

1. How can passive BCIs enhance user experience in everyday human-machine interactions?
2. What are the ethical implications of using cognitive monitoring in passive BCIs for healthy users?
3. In what ways can passive BCI technology be integrated into existing systems to improve functionality?
4. What challenges do researchers face when developing passive BCIs for diverse user groups?
5. How does the effectiveness of passive BCIs compare to traditional active BCIs in terms of user engagement and performance?

Key Findings

Key Insights

1. Research Background and Objectives
   The study addresses the evolving landscape of brain-computer interface (BCI) technology, particularly in the context of cognitive monitoring. Traditional BCIs require users to consciously send commands to interact with computers, limiting their application scope. The authors propose an innovative approach dubbed "passive BCI," which leverages real-time brain signal decoding (RBSD) to glean insights about a user's cognitive state without requiring explicit commands. The objective is to enhance human-machine interaction by providing systems with a deeper understanding of user intentions, emotional states, and situational interpretations.

2. Main Methods and Findings
   The paper reviews existing studies that employ passive BCI technology, highlighting its potential to enrich human-machine systems. By fusing cognitive monitoring with BCI, the research demonstrates how automated RBSD can provide continuous feedback about a user's mental state. This integration allows technical systems to adapt and respond to cognitive and emotional signals from users, thereby creating a more intuitive interaction paradigm. The findings indicate that passive BCIs can function effectively in applications for healthy users, suggesting a framework that categorizes BCI applications, emphasizing the unification of active and passive modalities.

3. Core Conclusions
   The study concludes that the integration of cognitive monitoring into BCI systems represents a significant advancement in human-machine interaction. Passive BCIs offer a promising method for interpreting user states without requiring direct input, thereby reducing cognitive load and enhancing usability. This paradigm shift may lead to more adaptive, responsive, and personalized technological environments, fostering a deeper connection between users and machines.

4. Research Significance and Impact
   The significance of this research lies in its potential to transform how users interact with technology. By providing systems that can intuitively respond to users' mental states, passive BCIs could enhance user experience across various fields, such as gaming, rehabilitation, and assistive technologies. Moreover, this approach broadens the applicability of BCI technology beyond traditional settings, allowing for more inclusive designs that cater to the needs of healthy users. The unifying framework proposed may also guide future research in BCI applications, paving the way for more sophisticated systems that leverage cognitive insights to improve human-machine collaboration.

Literatures Citing This Work

1. A Dry EEG-System for Scientific Research and Brain-Computer Interfaces. - Thorsten Oliver Zander;Moritz Lehne;Klas Ihme;Sabine Jatzev;Joao Correia;Christian Kothe;Bernd Picht;Femke Nijboer - Frontiers in neuroscience (2011)
2. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience. - Ole Jensen;Ali Bahramisharif;Robert Oostenveld;Stefan Klanke;Avgis Hadjipapas;Yuka O Okazaki;Marcel A J van Gerven - Frontiers in psychology (2011)
3. Switching between Manual Control and Brain-Computer Interface Using Long Term and Short Term Quality Measures. - Alex Kreilinger;Vera Kaiser;Christian Breitwieser;John Williamson;Christa Neuper;Gernot R Müller-Putz - Frontiers in neuroscience (2011)
4. Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI. - Sarah D Power;Azadeh Kushki;Tom Chau - BMC research notes (2012)
5. Collaborative filtering for brain-computer interaction using transfer learning and active class selection. - Dongrui Wu;Brent J Lance;Thomas D Parsons - PloS one (2013)
6. Cognitive-motor brain-machine interfaces. - Ariel Tankus;Itzhak Fried;Shy Shoham - Journal of physiology, Paris (2014)
7. Comparison of sensor selection mechanisms for an ERP-based brain-computer interface. - David Feess;Mario M Krell;Jan H Metzen - PloS one (2013)
8. Decoding speech perception by native and non-native speakers using single-trial electrophysiological data. - Alex Brandmeyer;Jason D R Farquhar;James M McQueen;Peter W M Desain - PloS one (2013)
9. Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns. - Chris Dijksterhuis;Dick de Waard;Karel A Brookhuis;Ben L J M Mulder;Ritske de Jong - Frontiers in neuroscience (2013)
10. Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually. - Elisabeth V C Friedrich;Christa Neuper;Reinhold Scherer - PloS one (2013)

... (179 more literatures)

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