Brain computer interface control via functional connectivity dynamics

Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin

doi:10.1016/j.patcog.2011.04.034

Cited by 94 publications

(75 citation statements)

References 31 publications

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“…Fourth, the performance of BCI systems would be higher by including frontally positioned electrodes as suggested by Leocani, Toro, Manganotti, Zhuang, and Hallett (1997). In association with this work, Daly, Nasuto, and Warwick (2012) recently reported that dynamics of inter-regional communication changes during real and imagined single finger taps compared to the rest state. The functional connectivity analysis incorporating the electrodes in frontal area should be possible to observe more carefully the time dependent phenomenon from motor intention, motor planning, and execution based on the dynamical networking of functional connectivity.…”

Section: Discussionsupporting

confidence: 51%

Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain–computer interface

Kondo

Saeki

Hayashi

et al. 2015

Human Movement Science

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Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI neurofeedback training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills.

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Section: Discussionsupporting

confidence: 51%

Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain–computer interface

Kondo

Saeki

Hayashi

et al. 2015

Human Movement Science

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“…Although there are no general guidelines, classification systems are considered acceptable when their AUC is higher than 0.7 [39]. In BCI applications, a threshold of 0.8 is typically used in order to obtain a level of performance that guarantees that the time that it takes to output a symbol (including corrections to errors made by the system) will not be too long to discourage users (e.g., [40], [41]). …”

Section: ) Detection Of the P300 Componentmentioning

confidence: 99%

Brain–Computer Interfaces for Detection and Localization of Targets in Aerial Images

Matran-Fernandez

Poli

2017

IEEE Trans. Biomed. Eng.

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Abstract-Objective. The N2pc event-related potential (ERP) appears on the opposite side of the scalp with respect to the visual hemisphere where an object of interest is located. We explored the feasibility of using it to extract information on the spatial location of targets in aerial images shown by means of a rapid serial visual presentation (RSVP) protocol using single-trial classification. Methods. Images were shown to 11 participants at a presentation rate of 5 Hz while recording electroencephalographic signals.With the resulting ERPs we trained linear classifiers for singletrial detection of target presence and location. We analysed the classifiers' decisions and their raw output scores on independent test sets as well as the averages and voltage distributions of the ERPs. Results. The N2pc is elicited in RSVP presentation of complex images and can be recognised in single trials (the median area under the receiver operating characteristic curve was 0.76 for left vs right classification). Moreover, the peak amplitude of this ERP correlates with the horizontal position of the target within an image. The N2pc varies significantly depending on handedness, and these differences can be used for discriminating participants in terms of their preferred hand. Conclusion and Significance. The N2pc is elicited during RSVP presentation of real complex images and contains analogue information that can be used to roughly infer the horizontal position of targets. Furthermore, differences in the N2pc due to handedness should be taken into account when creating collaborative brain-computer interfaces.

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“…Many human brain-computer interfaces are used for therapeutic purposes, in order to overcome a medical/neurological problem, one example being, as will be discussed shortly, deep brain stimulation electrodes employed to overcome the effects of Parkinson's disease (Pinter et al 1999, Pan et al 2012, Wu et al 2010a, or the use of external electrodes to understand the functioning of parts of the brain (Daly et al 2012). However it is possible to consider employing such technology in alternative ways to give individuals abilities not normally possessed by humanshuman enhancement!…”

Section: General-purpose Brain Implantsmentioning

confidence: 99%

The disappearing human–machine divide

Warwick

2013

Self Cite

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In this article a look is taken at some of the different ways in which the human–machine divide is rapidly disappearing. In each case the technical basis is described and then some of the implications are also considered. In particular results from experiments are discussed in terms of their meaning and application possibilities. The article is written from the perspective of scientific experimentation opening up realistic possibilities to be faced in the future, rather than giving conclusive comments. In each case consideration is also given to some of the philosophical questions that arise.

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Brain computer interface control via functional connectivity dynamics

Cited by 94 publications

References 31 publications

Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain–computer interface

Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain–computer interface

Brain–Computer Interfaces for Detection and Localization of Targets in Aerial Images

The disappearing human–machine divide

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