Brain-Computer Interface—a new communication device for handicapped persons

Pfurtscheller, Gert; Flotzinger, Doris; Kalcher, J.

doi:10.1006/jmca.1993.1030

Cited by 352 publications

(190 citation statements)

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“…This fact should be taken into account in the evaluation of the system. This problem has been mentioned by several other authors, including Burl et al (1998), Cherkauer andShavlik (1994), Ezawa et al (1996), Fawcett and Provost (1997), Kubat, Pfurtscheller and Flotzinger (1994), and Pfurtscheller, Flotzinger and Kalcher (1992). For instance, in the SKICAT system (Fayyad, Weir & Djorgovski, 1993), the "batches" were plates, from which image regions were selected.…”

Section: Key Problem Characteristicsmentioning

confidence: 98%

Untitled

1998

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Abstract. During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing.

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Section: Key Problem Characteristicsmentioning

confidence: 98%

Untitled

1998

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“…Noninvasive BCIs use electroencephalographic (EEG) activity recorded from the scalp (Farwell and Donchin, 1988;Wolpaw et al, 1991;Sutter, 1992;McFarland et al, 1993McFarland et al, , 2008Pfurtscheller et al, 1993Pfurtscheller et al, , 2000McFarland, 1994, 2004;Birbaumer et al, 1999;Millán et al, 2004;Kübler et al, 2005;Blankertz et al, 2006;Vaughan et al, 2006;Müller et al, 2008). Although EEG-based BCIs support higher performance than often assumed, the acquisition of high levels of brain-based control typically requires extensive user training, and BCI performance can also be variable.…”

Section: Translating Microscale Neural Interface Technologies For CLImentioning

confidence: 99%

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Kipke¹,

Shain²,

Buzsáki³

et al. 2008

J. Neurosci.

262

176

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IntroductionTechnological advances in neural interfaces are providing increasingly more powerful "toolkits" of designs, materials, components, and integrated devices for establishing high-fidelity chronic neural interfaces. For a broad class of neuroscience studies, the primary requirements of these interfaces include recording and/or stimulating from a number of discretely sampled volumes at requisite spatial resolutions for specific periods of time. Translational and clinical applications present additional requirements for safety, usability, reliability, patient acceptance, and cost effectiveness. Innovative solutions result from the constructive tension between ever-increasing application requirements and incorporation of technological advances into usable devices. The purpose of this minireview is to present snapshots of the current state-of-the-art in chronic, microscale neural interfaces by highlighting several leading neuroscience applications and discussing their implications for next-generation interface devices.

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“…A brain-computer interface (BCI) is operated using braingenerated information, thereby providing the user with an alternative communication or control channel that does not require the brain's normal peripheral nerve and muscle pathways [1]. Operating a BCI is a multi-step process [2], which involves acquiring a signal from the user's brain, preprocessing it to reduce artifacts, extracting useful features that can be used to make inferences about the user's current cognitive state, and then translating these features into a form that can be used to communicate with an external device, and finally, providing feedback so the user can see that the BCI is working (Fig.…”

Section: Introductionmentioning

confidence: 99%

Improving SSVEP-BCI performance using pre-trial normalization methods

Henshaw

Liu

Romano

2017

2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

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Abstract-A brain-computer interface (BCI) enables users to communicate through a computer using only their brain signals, by extracting brain signal features containing information representative of the user's intent, and can be used in a wide variety of areas such as entertainment, rehabilitation, or assistive technologies. In this paper, two novel normalization methods are assessed with the aim of improving the quality of the extracted features: Baseline-Corrected canonical correlation analysis (BC-CCA), and Scaled CCA. Both methods are found to be able to improve classification accuracy in conditions using frequencies with a large range, whilst BC-CCA is the superior of the two, improving SSVEP detection accuracy by as much as 9.22%.

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Brain-Computer Interface—a new communication device for handicapped persons

Cited by 352 publications

References 0 publications

Untitled

Untitled

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Improving SSVEP-BCI performance using pre-trial normalization methods

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