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Journal ArticleDOI

Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface.

Noman Naseer, +1 more
- 11 Oct 2013 - 
- Vol. 553, pp 84-89
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TLDR
The results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2s span and/or the time span after the peak value.
About
This article is published in Neuroscience Letters.The article was published on 2013-10-11. It has received 260 citations till now. The article focuses on the topics: Functional near-infrared spectroscopy & Motor imagery.

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Citations
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Journal ArticleDOI

fNIRS-based brain-computer interfaces: a review

TL;DR: In this paper, the most common brain areas for fNIRS-based BCI are the primary motor cortex and prefrontal cortex, and the motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided.
Journal ArticleDOI

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

TL;DR: The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.
Journal ArticleDOI

Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI.

TL;DR: The results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs and concurrently measure and discriminate fNirS signals evoked by three different mental activities.
Journal ArticleDOI

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

TL;DR: A novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals is presented.
Journal ArticleDOI

Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface

TL;DR: It is observed and verified, using the linear discriminant analysis (LDA) and the support vector machine (SVM) classifications, that the cortical hemodynamic responses for making a “yes” decision are distinguishable from those forMaking a ‘no’ decision.
References
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Journal ArticleDOI

Brain-computer interfaces for communication and control.

TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
Journal ArticleDOI

Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters

TL;DR: Observations by infrared transillumination in the exposed heart and in the brain in cephalo without surgical intervention show that oxygen sufficiency for cytochrome a,a3, function, changes in tissue blood volume, and the average hemoglobin-oxyhemoglobin equilibrium can be recorded effectively and in continuous fashion for research and clinical purposes.
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A review of classification algorithms for EEG-based brain–computer interfaces

TL;DR: This paper compares classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG) in terms of performance and provides guidelines to choose the suitable classification algorithm(s) for a specific BCI.
Journal ArticleDOI

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

TL;DR: The results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.
Journal ArticleDOI

Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study

TL;DR: The hypothesis that MI and MP involve overlapping neural networks in perirolandic cortical areas is supported by functional magnetic resonance imaging techniques.
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