Motor imagery based brain–computer interfaces: An emerging technology to rehabilitate motor deficits

Alonso-Valerdi, Luz María; Salido-Ruiz, Ricardo A.; Ramírez-Mendoza, Ricardo A.

doi:10.1016/j.neuropsychologia.2015.09.012

Cited by 75 publications

(37 citation statements)

References 48 publications

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“…In most cases, MI was used to generate the commands for communication (Machado et al, 2010; Bi et al, 2013; Hwang et al, 2013; Ahn and Jun, 2015; Maria Alonso-Valerdi et al, 2015). However, it is difficult for patients to perform an MI activity.…”

Section: Advantages Of Hbcimentioning

confidence: 99%

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

2017

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In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

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Section: Advantages Of Hbcimentioning

confidence: 99%

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

2017

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“…Advancements such as these, in conjunction with the algorithm presented in this paper, will serve to improve the detection, classification, and evaluation of MI signals. This, in turn, can lead to improvements in EEG-based rehabilitation technologies, improving both the prediction and elicitation of motor recovery in a multitude of diseases worldwide [40]. …”

Section: Discussionmentioning

confidence: 99%

Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification

She

Gan

et al. 2016

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Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge. Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data. The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks. The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets.

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“…The paper "Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits", by Luz Maria Alonso-Valerdi.,et all. [1] discusses the various classes and types of BMI.…”

Section: IImentioning

confidence: 99%

A Survey on Various Motor Imagery-Based Brain-Computer Interface Techniques

2017

IJRTER

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Abstract-Non-invasive EEG-based motor imagery brain-computer interface (MI-BCI) holds promise to effectively restore motor control to motor impaired patient. When the sensory-motor integration system is malfunctioning it causes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to restore the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability among the worldwide populations. Therefore, this paper aims to review MI-BIC techniques, in order to evaluate the suitability and reliability of this technology.

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Motor imagery based brain–computer interfaces: An emerging technology to rehabilitate motor deficits

Cited by 75 publications

References 48 publications

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification

A Survey on Various Motor Imagery-Based Brain-Computer Interface Techniques

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