Graz-BCI: state of the art and clinical applications

Pfurtscheller, Gert; Neuper, Christa; Müller, Gernot; Obermaier, B.; Krausz, Gunther; Schlögl, Alois; Scherer, Reinhold; Gramlich, Bernhard; Keinrath, Claudia; Skliris, Dimitris; Wortz, M.; Supp, Gernot G.; Schrank, C.

doi:10.1109/tnsre.2003.814454

Cited by 238 publications

(139 citation statements)

References 18 publications

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“…1, middle arrow) was pursued as an alternative to somatic control. To date, a number of brain signal components have demonstrated the ability to be self regulated, including electroencephalographic (EEG) components such as visual alpha rhythms, slow cortical potentials (SCP) [17] and sensorimotor rhythms (SMRs) [18,19], neuronal firing as recorded by invasively implanted electrode arrays [20][21][22], and signals measured via magnetoencephlography (MEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI) [23,24] and near-infrared spectroscopy (NIRS) [25,26]. Many of these signals have been harnessed to create a brain-controlled access pathway for the individuals of the target population, often referred to as brain-computer interfaces (BCIs) or brainmachine interfaces (BMIs).…”

Section: Current Access Pathways To Assistive Technologiesmentioning

confidence: 99%

Peripheral Autonomic Signals as Access Pathways for Individuals with Severe Disabilities: A Literature Appraisal

Blain¹,

Chau

Mihailidis³

2008

TOREHJ

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Many individuals with severe and multiple disabilities do not have an access pathway that enables them to interface with their environment because they are not afforded a binary switch that they can reliably control. While recent research has focused on the self-regulation of central signals of the autonomic nervous system (ANS) to create braincomputer interfaces (BCIs) for these individuals, there has been less focus on the peripheral signals of the ANS as an access pathway. An appraisal of the literature in the areas of biofeedback, polygraphy and mental exercises uncovered considerable evidence that peripheral ANS signals can be voluntarily controlled and thus have the potential to be used as an access pathway by the target population. However, the issues of speed, metabolic noise and pathological change must be addressed before peripheral ANS signals can be used as either a complementary or alternative access pathway to existing brain-computer interfaces.

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Section: Current Access Pathways To Assistive Technologiesmentioning

confidence: 99%

Peripheral Autonomic Signals as Access Pathways for Individuals with Severe Disabilities: A Literature Appraisal

Blain¹,

Chau

Mihailidis³

2008

TOREHJ

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“…Event-related potentials, event-related synchronization and desynchronization (ERS/ERD) [2], slow cortical potentials [3], P300 evoked potentials [4] and Steady-State Visual Evoked Potentials (SSVEP) [5]- [9] are signals that have been utilized in non-invasive BCIs. The main goal of a BCI is to support people with severe motor disabilities [10], [ 1], for example by allowing them to communicate by writing text messages, surfing the internet and controlling external devices such as neuroprotheses, wheelchairs, robots or simply to shift the sitting position. An SSVEP-based BCI used to navigate a menu system and to execute high-level commands such as pouring water into a glass.…”

Section: Introductionmentioning

confidence: 99%

Brain-Computer Interface for high-level control of rehabilitation robotic systems

Valbuena

Cyriacks

Friman

et al. 2007

2007 IEEE 10th International Conference on Rehabilitation Robotics

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Abstract-In this work, a Brain-Computer Interface (BCI) based on Steady-State Visual Evoked Potentials (SSVEP) is presented as an input device for the human machine interface (HMI) of the semi-autonomous robot FRIEND II. The role of the BCI is to translate high-level requests from the user into control commands for the FRIEND II system. In the current application, the BCI is used to navigate a menu system and to select commands such as pouring a beverage into a glass. The low-level control of the test platform, the rehabilitation robot FRIEND II, is executed by the control architecture MASSiVE, which in turn is served by a planning instance, an environment model and a set of sensors (e.g., machine vision) and actors. The BCI is introduced as a step towards the goal of providing disabled users with at least 1.5 hours independence from care givers.

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“…Noninvasive BCIs, which derive the user's intent from scalp-recorded electroencephalographic (EEG) activity, are already in use for basic communication and control (2,3). Invasive BCIs, which derive the user's intent from neuronal action potentials or local field potentials recorded within the brain, are being studied mainly in nonhuman primates (4)(5)(6)(7)(8)(9)(10)(11)(12).…”

mentioning

confidence: 99%

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Wolpaw

McFarland

2004

Proc. Natl. Acad. Sci. U.S.A.

1,289

971

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Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.brain-machine interface ͉ electroencephalography

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Graz-BCI: state of the art and clinical applications

Cited by 238 publications

References 18 publications

Peripheral Autonomic Signals as Access Pathways for Individuals with Severe Disabilities: A Literature Appraisal

Peripheral Autonomic Signals as Access Pathways for Individuals with Severe Disabilities: A Literature Appraisal

Brain-Computer Interface for high-level control of rehabilitation robotic systems

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

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