Brain–computer communication: Unlocking the locked in.

Kübler, Andrea; Kotchoubey, Boris; Kaiser, Jochen; Wolpaw, Jonathan R.; Birbaumer, Niels

doi:10.1037/0033-2909.127.3.358

Cited by 459 publications

(276 citation statements)

References 96 publications

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“…This might allow a patient to answer one yes/no question per minute or spell one letter every 5 minutes. Articles by groups with patient experience have described even slower systems that patients chose to continue using (e. g., Kübler et al, 2001;Kübler et al, 2005). However, a brief unpublished anecdote might provide a more direct response to this question.…”

Section: 3: Implications For Online Controlmentioning

confidence: 99%

“…Brain computer interface (BCI) systems translate direct measures of brain activity into messages or commands. A variety of BCI systems have been described in the literature and typically are categorized according to the cognitive and neural activity needed for control (for review, see Kübler et al, 2001;Wolpaw et al, 2002;Allison, 2003;Kübler and Neumann, 2005;Jackson et al, 2006;Allison et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…SSVEP BCI articles typically note that subjects were told to shift gaze (Sutter et al, 1992;Middendorf et al, 2000;Cheng et al, 2002;Gao et al, 2003). Two BCI reviews (Kübler et al, 2001;Wolpaw et al, 2002) define SSVEP BCIs as "dependent" BCIs, meaning that they use EEG features that depend on muscle activity and thus would not work in patients without control over that activity. SSVEP BCI development would then be less important, as other assistive technologies based on gaze direction might be more effective (Cook and Hussey, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Towards an independent brain–computer interface using steady state visual evoked potentials

Allison

McFarland

Schalk

et al. 2008

Clinical Neurophysiology

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285

183

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Objective-Brain -computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.Methods-Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of twelve one-minute runs.Results-Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.Conclusions-The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.Significance-SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

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Section: 3: Implications For Online Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards an independent brain–computer interface using steady state visual evoked potentials

Allison

McFarland

Schalk

et al. 2008

Clinical Neurophysiology

Self Cite

285

183

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“…This intent is estimated from brain signals measured via signals from the scalp or from invasive techniques, cf [6,17,32] for an overview. A significant challenge in designing a BCI is to balance the technological complexity of interpreting the user's brain signals with the amount of user training required for successful operation of the interface.…”

Section: Introductionmentioning

confidence: 99%

Towards adaptive classification for BCI

Shenoy

Krauledat

Blankertz³

et al. 2006

J. Neural Eng.

387

330

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Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.

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“…Polich [27] states that memory processing and selective attention have a direct bearing on controlling a P300 matrix. Kübler et al, [28] highlight the importance of concentration when operating such a system in order to ignore any additional environmental noises, to focus on the symbol that you intend to select and to internally count each time it is illuminated. It is also important to learn how to interact with the system effectively and have the motivation to continue to engage with system [29].…”

Section: ____________________________________________________________mentioning

confidence: 99%

P300 Brain Computer Interface Control after an Acquired Brain Injury

Daly¹,

Armstrong²,

Thomson³

et al. 2015

IJRITCC

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Abstract-Brain-Computer Interfaces (BCI) are systems that can be controlled by the user through harnessing their brain signals. Extensive research has been undertaken within a laboratory setting with healthy users to illustrate the usability of such systems. To bring these systems to users with severe disabilities it is necessary to develop simple, easy to use systems that can be operated b y non-experts outside of the lab and are evaluated by real end users preferably through a user centered design approach. This paper presents a user centered evaluation of a P300 BCI operated by non-expert users in a rehabilitation center with a control group of f i v e healthy participants w i t h o u t acquired brain injury ( A B I ) and five end users with ABI. Each participant aimed to complete the 30-ste p pr ot ocol three separate ti mes an d rate his or her satis fa cti on fr om 0 to 10 on the Visual Anal ogue S cale after each session. Participants then rated their satisfaction with the BCI on the extende d QUES T 2.0 and a customized usability questionnaire. The results indicated that end-users were able to achieve an average accuracy of 55% compared to the control group that reported an average of 78%. The findings indicated that participants were satisfied with the BCI but felt frustrated when it did not respond to their commands. This work was phase one of three to move the BCI system into end users homes. Key recommendations for advancing the P300 BCI towards an easy to use, home-based system were identified, including reducing the complexity of the setup, ensuring the system becomes more responsive and increasing the overall functionality.

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Brain–computer communication: Unlocking the locked in.

Cited by 459 publications

References 96 publications

Towards an independent brain–computer interface using steady state visual evoked potentials

Towards an independent brain–computer interface using steady state visual evoked potentials

Towards adaptive classification for BCI

P300 Brain Computer Interface Control after an Acquired Brain Injury

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