Toward smarter BCIs: extending BCIs through hybridization and intelligent control

Allison, Brendan Z.; Leeb, Robert; Brunner, Clemens; Müller-Putz, Gernot; Bauernfeind, Günther; Kelly, John W.; Neuper, Christa

doi:10.1088/1741-2560/9/1/013001

Cited by 93 publications

(48 citation statements)

References 49 publications

(75 reference statements)

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“…Two fusion techniques were evaluated: Gaze-SSVEP and an ERD/ERS-SSVEP, called a physiological and pure interface [41], respectively. The performance measurements show that the sBCI system provides an effective environmental control method for all six subjects.…”

Section: Discussionmentioning

confidence: 99%

sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

et al. 2015

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Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI) that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP)-BCI, an Event-related (de)-synchronization (ERD/ERS)-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI) system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a "hands-free" handling of devices is necessary. OPEN ACCESSMicromachines 2015, 6 292

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Section: Discussionmentioning

confidence: 99%

sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

et al. 2015

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“…Müller-Putz et al (2011) note that BCIs, often using EEG signals for input, are a suitable options for people with motor disabilities to give them a method of control without the need for movement. Allison et al (2012) provide a recent survey of brain control research. Current brain-machine interface technology has limitations, and researchers have overcome these limitations by using it in multimodal settings and in a semi-autonomous framework (McMullen et al, 2014;Müller-Putz et al, 2011;Zander et al, 2010).…”

Section: Brain-computer Interfacesmentioning

confidence: 99%

Fuzzy Logic Based Eye-Brain Controlled Web Access System

Mirchandani¹

2015

CIS

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Accessing the Web is crucially important in today's society because of communication, education, and entertainment opportunities. Paralyzed or paretic individuals are unable to capitalize on these opportunities using traditional human-computer interaction methods. Thus, I develop a low-cost web browsing system for such individuals, integrating eye and brain control in a novel fashion to relay and interpret navigation commands. The system combines gaze position estimates obtained from a new image processing algorithm with brain concentration levels sensed and transmitted by an electroencephalogram headset. Since user intent may itself be uncertain, the system incorporates a novel fuzzy logic algorithm for combining brainwave and eye position inputs to determine the user's targeted hyperlink. The algorithm adopts an approach based on exponential smoothing to efficiently keep a record of historical signals. Experimental evaluation established that the first attempt success rate of the system lies between 87% and 95% with 95% confidence. Error recovery accuracy is 98.4%, resulting in a second attempt success rate of 99.1%. This assistive technology, using off-the-shelf components, costs less than $100 excluding the tablet computer, and has the potential to affordably open up the Web to millions of individuals.

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“…For the correction of inaccuracies of an isolated EEG signal in a low-signal stimulus, measured by using EMG, it is possible to use a combination of these approaches [13,14]. To compensate the lack of EMG signals in case of paresis or lack of limbs can be additionally used the EEG signal.…”

Section: Introductionmentioning

confidence: 99%