Brain Computer Interface: A Review

Fouad, Mohamed Mostafa; Amin, Khalid; El-Bendary, Nashwa; Hassanien, Aboul Ella

doi:10.1007/978-3-319-10978-7_1

Cited by 60 publications

(28 citation statements)

References 77 publications

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“…These results show that young people can be more successful in a machine/robot control that works with thought. BMI, also known as BCI, is a system that enables people to interact with their environment by using control signals generated from EEG activity, without utilizing the muscular and the motor neural systems [27][28][29]. EEG is a measurement of electrical stimuli of neurons.…”

Section: Resultsmentioning

confidence: 99%

Beyin Makine Arayüzü kullanımında Yaşın Etkisi

Alkiş

Koç

2019

Muş Alparslan Üniversitesi Fen Bilimleri Dergisi

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Brain Machine Interface (BMI) especially used for disabled people and military services. However, in the literature review, no study was detected on the relationship between the age of the person using the device and the performance of it. The aim of this study is to detect whether age is important in controlling a robot using BMI or in which age range this control is more efficient. The study was carried out with 45 healthy male subjects (age range: 7-60). The focusing and activating time of each subject was recorded and analysed. The analysis results showed that this time was the shortest in children and the longest in adults. The study results indicated that the time to focus and activate the device increased in parallel with the age, and hence, the children and the young were much better at controlling or activating an external device through BMI.

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Section: Resultsmentioning

confidence: 99%

Beyin Makine Arayüzü kullanımında Yaşın Etkisi

Alkiş

Koç

2019

Muş Alparslan Üniversitesi Fen Bilimleri Dergisi

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“…There are many source separation methods that range in assumptions and implementation details. The brain–computer interface community has developed many strategies for dimensionality reduction and source separation as it relates to classification of states based on multichannel EEG signals (Fouad et al ., ). Generalized eigendecomposition is used in many of these approaches (where it is sometimes called common spatial pattern analysis), because it tends to be a fast, robust and efficient method.…”

Section: Discussionmentioning

confidence: 97%

Using spatiotemporal source separation to identify prominent features in multichannel data without sinusoidal filters

Cohen

2017

Eur J of Neuroscience

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The number of simultaneously recorded electrodes in neuroscience is steadily increasing, providing new opportunities for understanding brain function, but also new challenges for appropriately dealing with the increase in dimensionality. Multivariate source separation analysis methods have been particularly effective at improving signal-to-noise ratio while reducing the dimensionality of the data and are widely used for cleaning, classifying and source-localizing multichannel neural time series data. Most source separation methods produce a spatial component (that is, a weighted combination of channels to produce one time series); here, this is extended to apply source separation to a time series, with the idea of obtaining a weighted combination of successive time points, such that the weights are optimized to satisfy some criteria. This is achieved via a two-stage source separation procedure, in which an optimal spatial filter is first constructed and then its optimal temporal basis function is computed. This second stage is achieved with a time-delay-embedding matrix, in which additional rows of a matrix are created from time-delayed versions of existing rows. The optimal spatial and temporal weights can be obtained by solving a generalized eigendecomposition of covariance matrices. The method is demonstrated in simulated data and in an empirical electroencephalogram study on theta-band activity during response conflict. Spatiotemporal source separation has several advantages, including defining empirical filters without the need to apply sinusoidal narrowband filters.

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“…The brain-computer-interface community has developed many strategies for dimensionality reduction and source separation as it relates to classification of states based on multichannel EEG signals (Fouad et al, 2014). Generalized eigendecomposition is used in many of these approaches (where it is sometimes called common spatial pattern analysis), because it tends to be a fast, robust, and efficient method.…”

Section: Comparison To Other Source Separation Methodsmentioning

confidence: 99%

Using spatiotemporal source separation to identify prominent features in multichannel data without sinusoidal filters

Cohen¹

2017

Preprint

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The number of simultaneously recorded electrodes in neuroscience is steadily increasing, providing new opportunities for understanding brain function, but also new challenges for appropriately dealing with the increase in dimensionality. Multivariate source-separation analysis methods have been particularly effective at improving signal-to-noise ratio while reducing the dimensionality of the data, and are widely used for cleaning, classifying, and source-localizing multichannel neural time series data. Most source-separation methods produce a spatial component (that is, a weighted combination of channels to produce one time series); here, this is extended to apply sourceseparation to a time series, with the idea of obtaining a weighted combination of successive time points, such that the weights are optimized to satisfy some criteria. This is achieved via a two-stage source-separation procedure, in which an optimal spatial filter is first constructed, and then its optimal temporal basis function is computed. This second stage is achieved with a time-delay-embedding matrix, in which additional rows of a matrix are created from time-delayed versions of existing rows. The optimal spatial and temporal weights can be obtained by solving a generalized eigendecomposition of covariance matrices. The method is demonstrated in simulated data and in an empirical EEG study on theta-band activity during response conflict. Spatiotemporal source separation has several advantages, including defining empirical filters without the need to apply sinusoidal narrowband filters.

show abstract

Brain Computer Interface: A Review

Cited by 60 publications

References 77 publications

Beyin Makine Arayüzü kullanımında Yaşın Etkisi

Beyin Makine Arayüzü kullanımında Yaşın Etkisi

Using spatiotemporal source separation to identify prominent features in multichannel data without sinusoidal filters

Using spatiotemporal source separation to identify prominent features in multichannel data without sinusoidal filters

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