A collaborative brain-computer interface

Wang, Yijun; Wang, Yu-Te; Jung, Tzyy‐Ping; Gao, Xiaorong; Gao, Shangkai

doi:10.1109/bmei.2011.6098286

Cited by 18 publications

(14 citation statements)

References 10 publications

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“…Additionally, when DWT is used, the choice of a suitable wavelet and threshold values is a crucial task to be considered [27]. Independent Component Analysis (ICA) has been widely used in EEG analysis for artifacts removal, SNR enhancement, and optimal electrodes selection [28,29], however, some minor drawbacks such as power spectrum corruption [30], or component localization [31] may be present. According to the references consulted, ICA has been mainly used in offline analysis.…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Rosas-Cholula

Ramírez-Cortés

Alarcón-Aquino

et al. 2013

Sensors

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This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

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Section: Introductionmentioning

confidence: 99%

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Rosas-Cholula

Ramírez-Cortés

Alarcón-Aquino

et al. 2013

Sensors

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“…al. [17] used a collaborative BCI to improve overall performance by integrating information from multiple users. Experiments with 15 subjects participating in a Go/NoGo decisionmaking experiment evaluated the collaborative method.…”

Section: Related Workmentioning

confidence: 99%

“…The main reason is the difficulty of getting pure EEG signals directly correlated with the given task. To overcome this problem, recently, researchers presented the idea of combining the signals from neural activity across multiple brains (e.g., see [4], [17], and [10]). The advantage of this is that the chances of getting noisy EEG signals at all times is slim.…”

Section: Introductionmentioning

confidence: 99%

Two Brains Guided Interactive Evolution

Kattan¹,

Doctor

Arif³

2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-In this paper, we show that it is possible to use electroencephalography (EEG) and multi-brain computing with two humans to guide an Interactive Genetic Algorithm (IGA) system. We show that combining neural activity across two brains increases accuracy to guide evolutionary search more effectively. The IGA system involves a simple task of evolving a polygon shape to approximate the shape of a target polygon. Two candidates visually inspected the evolved polygons and mentally ranked them (independently from each other) from 1−10 based on their similarity to the target polygon. In parallel, the IGA system evaluated the fitness of evolved polygons using a standard fitness function. The IGA system was run for a few generations, before evolution was paused and EEG signals were collected from the two candidates. The collected EEG signals were used to train a regression model that received unseen EEG as input and mapped this into fitness values. The trained model was then used to guide the IGA solely by using the EEG signals. Off-line experimental results showed that it was possible to build better regression models that are trained using two EEG signals to capture participants evaluation of fitness. This paper demonstrates the possibility of a new domain of applications for interactive evolution where standard fitness calculations can be replaced with multiple EEG signals for guiding an optimisation process.

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“…Additionally, the distributed multiple-subject paradigm was performed through an ensemble LDA classifier that consists of multiple sub-classifiers and a voting method [18]. The weighted voting can be described in Fig.…”

Section: B Procedures and Paradigm For Discrimination Of Motor Imagermentioning

confidence: 99%

Effective user training for motor imagery based brain computer interface with object-directed 3D visual display

Liang

Choi

Qin

et al. 2014

2014 7th International Conference on Biomedical Engineering and Informatics

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Effective user training could help us to improve the discrimination performance of our intention in brain computer interface (BCI). This paper aims to differentiate users left or right hand motor imagery (MI) tasks with different scenarios in 3D virtual environment, as non-object-directed (NOD) scenario, static-object-directed (SOD) scenario and dynamicobject-directed (DOD) scenario respectively. The results have significant differences by applying these three scenarios. Both SOD and DOD scenarios pro-vide better classification accuracy, shorten single-trial period, and need smaller training samples comparing with the NOD case. We conclude that improving visual display may facilitate learning to use a BCI. Further comparing these results between single-subject and multiple-subject paradigm of BCI, we verify better classification performance could also be achieved by the multiple-subject paradigm. We believe these findings have the potential to improve discrimination performance of users intention for EEG-based BCI applications.

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A collaborative brain-computer interface

Cited by 18 publications

References 10 publications

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Two Brains Guided Interactive Evolution

Effective user training for motor imagery based brain computer interface with object-directed 3D visual display

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