Brain-Computer Interface Research: A State-of-the-Art Summary -2

Brain-Computer Interface Research
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Therefore we suggest that such a learning-based approach can achieve sensory substitution and augmentation of vision, the two desired properties of an artificial sensory feedback signal for clinical motor neural prostheses. For the past decade, our group worked towards the development of a non-invasive BCI system for neuromodulation. Until recently, BCIs have been used mainly for communication and replacement or restoration of lost functions for severely disabled people.

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Using a BCI for neuromodulation requires that the protocol closely matches the steps involved in the motor learning process. However, the underlying mechanisms of motor learning in humans remain elusive, though several possibilities have been proposed. Of these, the most promising was proposed by Hebb The organization of behavior: a neuropsychological theory, vol. If this occurs repetitively with the necessary intensity, synaptic strength is increased.

To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.

That is, the same input will produce a greater output. Stefan et al.

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Brain J Neurol pt 3 —, were the first to investigate this concept non-invasively in humans, and it has now become accepted that it closely matches what occurs during motor learning. When the onset of the intended movement is detected, it is used to drive an external device that produces the intended movement. Through this process, the user is provided with the necessary proprioceptive feedback, timed to coincide with the onset of the intended movement, so that the Hebbian principle of associativity is satisfied.

A State-of-the-Art Summary 3

In this chapter, we outline the development of this BCI system for neuromodulation and show that it can be used to drive any external device while satisfying all the main criteria necessary for a full BCI application, namely: accuracy, flexibility, rapid control, and robustness. We present a radically new way of recording EEG comfortably and unobtrusively over long time-periods in natural environments. This break-through has been achieved using electrodes embedded on a customized earpiece as typically used in hearing aids Ear-EEG.

Brain–Computer Interface Allows Speediest Typing to Date - Scientific American

We illustrate the potential of Ear-EEG as an enabling technology for a number of uses beyond traditional BCI, which are currently limited by the inconvenience of standard EEG recording methods. We show that Ear-EEG enables both conventional BCI and next-generation applications such as the evaluation of hearing capability and the monitoring of fatigue and drowsiness.

Stroke patients must exercise intensely with rehabilitation robots to achieve satisfactory rehabilitation outcome, but ensuring appropriate exercise difficulty is a challenging task.

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Brain-computer interfaces would be suitable for such difficulty adaptations since they capture both conscious and subconscious aspects of workload, but have seen little use in rehabilitation. This chapter reviews previous work on passive brain—computer interfaces and highlights the practical challenges of applying the technology to motor rehabilitation. Preliminary results of a study on workload estimation in a rehabilitation robot with healthy subjects are then presented.

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Semantic Scholar extracted view of "Brain-Computer Interface Research - A State -of-the-Art Summary" by Christoph Guger et al. Request PDF on ResearchGate | On Jan 1, , Christoph Guger and others published Brain-Computer Interface Research: A State-of-the-Art.

Adaptive stepwise regression is used to estimate different types of workload from electroencephalography signals recorded at different sites. Results show that electroencephalography can achieve more accurate workload estimation than autonomic nervous system responses and that adaptive estimation methods can further improve accuracy. However, the number of electrode sites needs to be reduced and issues such as motion artefacts must be resolved before passive brain-computer interfaces can be used in motor rehabilitation. Brain-machine interfaces BMIs have largely been designed for performing single-targeted movements.

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