Towards an EEG-based brain-computer interface for online robot control

Li, Yantao; Zhou, Gang; Graham, Daniel; Holtzhauer, Andrew

doi:10.1007/s11042-015-2717-z

Cited by 27 publications

(13 citation statements)

References 37 publications

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“…Recently, BCIs also have been employed for healthy individuals’ entertainment purposes (Ahn et al, 2014 ; Bai et al, 2015 ; Li et al, 2016 ), though this is not the main priority of BCI research. In any case, the feasibility of brain-controlled video games has been demonstrated using EEG-BCI; however, no actual hBCI application has been introduced to date yet.…”

Section: Applicationsmentioning

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: Applicationsmentioning

confidence: 99%

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

2017

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“…PCA serves the speed-boosting of the fitting of the classifier by dimensionality reduction. PCA converts data linearly into new features that are not correlated with each other by doing the orthogonal transformation [Li et.al, 2015].…”

Section: Methodsmentioning

confidence: 99%

Development of LSTM&CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks

Uyulan

2020

Preprint

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Recent studies underline the contribution of brain-computer interface (BCI) applications to the enhancement process of the life quality of physically impaired subjects. In this context, to design an effective stroke rehabilitation or assistance system, the classification of motor imagery (MI) tasks are performed through deep learning (DL) algorithms. Although the utilization of DL in the BCI field remains relatively premature as compared to the fields related to natural language processing, object detection, etc., DL has proven its effectiveness in carrying out this task.In this paper, a hybrid method, which fuses the one-dimensional convolutional neural network (1D CNN) with the long short-term memory (LSTM), was performed for classifying four different MI tasks, i.e. left hand, right hand, tongue, and feet movements. The time representation of MI tasks is extracted through the hybrid deep learning model training after principal component analysis (PCA)-based artefact removal process. The performance criteria given in the BCI Competition IV dataset A are estimated. 10-folded Cross-validation (CV) results show that the proposed method outperforms in classifying electroencephalogram (EEG)-electrooculogram (EOG) combined motor imagery tasks compared to the state of art methods and is robust against data variations. The CNN-LSTM classification model reached 95.62 % (±1.2290742) accuracy and 0.9462 (±0.01216265) kappa value for datasets with four MI-based class validated using 10-fold CV. Also, the receiver operator characteristic (ROC) curve, the area under the ROC curve (AUC) score, and confusion matrix are evaluated for further interpretations.

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“…Motion intent can be also classified by linear discriminant analysis (LDA) [21,25,26,31]. A recent presented application of k-means clustering and Principal Component Analysis (PCA) for steering of a simple robot [28] with a mental binary trigger, tested on 6 users. BCI was also applied in a computer game scenario with biofeedback and classification based on Regularized Fisher's Discriminant (RFD) [22].…”

Section: Eeg Signal Processing Approachesmentioning

confidence: 99%

“…Every signal is decomposed into the time-frequency domain (TF): it is split into frequency bands, following the standard EEG ranges: delta (2-4 Hz), theta (4-7 Hz), alpha (8-15 Hz), beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and gamma (30-59 Hz). The next step is extraction of filtered signals envelopes using Hilbert transform [29], being the indication of the overall activity in the particular frequency band.…”

Section: Processing In the Time-frequency Domainmentioning

confidence: 99%

Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

Szczuko

2017

Multimed Tools Appl

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Rough set-based approach to the classification of EEG signals of real and imaginary motion is presented. The pre-processing and signal parametrization procedures are described, the rough set theory is briefly introduced, and several classification scenarios and parameters selection methods are proposed. Classification results are provided and discussed with their potential utilization for multimedia applications controlled by the motion intent. Accuracy metrics of classification for real and imaginary motion obtained with different parameter sets are compared. Results of experiments employing recorded EEG signals are commented and further research directions are proposed.

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Towards an EEG-based brain-computer interface for online robot control

Cited by 27 publications

References 37 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

Development of LSTM&CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks

Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

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