An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential

Li, Yuanqing; Long, Jinyi; Yu, Tao; Yu, Zhuliang; Wang, Chuanchu; Zhang, Haihong; Guan, Cuntai

doi:10.1109/tbme.2010.2055564

Cited by 270 publications

(172 citation statements)

References 32 publications

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“…Because electroencephalography (EEG) is less expensive and easier to use in practice than other recording techniques of brain activities it has been the most popular one employed to develop BCIs EEG is most widely used for measuring brain signals (Li et al, 2010). The main EEG signals used for control are P300 visual evoked potentials (Farwell and Donchin, 1988), steady state visual evoked potential (Middendorf et al, 2000), and event related (de)synchronisation (ERD/ERS) produced by motor imaginary (Pfurtscheller and Lopes da Silva, 1999).…”

Section: Introductionmentioning

confidence: 99%

The types of hybrid modalities in brain-computer interface systems: a review

Singla

Jatana

2017

IJBET

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Brain-Computer Interface (BCI) is a young research area for researchers. Increasing number of research activities improves several areas such as signal acquisition techniques, hardware development, machine learning, and signal processing and system integration. However, there are many disadvantages of conventional BCI approaches. For example, Motorimagery based BCIs requires extensive training of the subjects, P-300 based BCIs still requires several stimulus repetitions to obtain reliable accuracy and in SSVEP stimulus; number of commands is limited by the number of stimulus frequencies and many more. To overcome these disadvantages and further improvement in performance of the system, an increasing number of researchers have begun to explore hybrid BCI approaches, in which multiple BCI approaches are incorporated in a single BCI system. The purpose of this paper is to give a brief introduction to the different types of hybrid BCI techniques. There are many different types of hybrid BCI that can be used in a wide range of applications. The paradigm design plays a very important role in the performance of hybrid BCIs.

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Section: Introductionmentioning

confidence: 99%

The types of hybrid modalities in brain-computer interface systems: a review

Singla

Jatana

2017

IJBET

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“…In order to achieve the goal of facilitating patients with severe disability a new life style, making them live independently by non-contact and no muscle action, developing a novel way to innovate the traditional human-computer interaction manner is becoming more and more fascinating [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Remote controller using steady state visual evoked potential

Song¹,

Wang²,

Wang³

et al. 2017

2nd Annual International Conference on Energy, Environmental &Amp; Sustainable Ecosystem Development (EESED 2016)

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This paper develops a new air-conditioning remote ccontrol system which based on brain waves and the Android system. First of all, the desired brain waves in EEG signals in the form of steady-state visual evoked is collected. By extracting and analysis the feature hidden in EEG signal, the system can identify the operation intention of volunteer to control air-conditioning machine, such as open or close and addition or subtraction about temperature. In addition, the control coding and wireless connections are implemented based on Bluetooth technology. Finally, we realize the Android mobile phones as the core of whole remote control. The eexperimental results show that brain wave control system of air conditioner remote is capable of stable and rapid detection of brain signals to control commands. Compared to commonly used buttons remote control, our designed system can realize the goal of non-contact control for air-conditioning.

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“…An EEG-based BCI system extracts features from scalp EEG and translates the features into commands. Applications include motor imagery-based BCIs [11,12], P300-based BCIs [13][14][15], Appl. Sci.…”

Section: Introductionmentioning

confidence: 99%

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

Liu

Wang

2016

Applied Sciences

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This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user's EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher's discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.

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An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential

Cited by 270 publications

References 32 publications

The types of hybrid modalities in brain-computer interface systems: a review

The types of hybrid modalities in brain-computer interface systems: a review

Remote controller using steady state visual evoked potential

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

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