An Asynchronous P300 BCI With SSVEP-Based Control State Detection

Panicker, Rajesh C.; Puthusserypady, Sadasivan; Sun, Yeneng

doi:10.1109/tbme.2011.2116018

Cited by 183 publications

(136 citation statements)

References 34 publications

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“…Recently, steady-state visual evoked potential (SSVEP) is combined with P300 ERP. The hybrid P300 and SSVEP paradigm is capable of producing the asynchronous control state [8] or increasing the speed [3]. Since EEG signals are much suffered from low SNR, multiple repeated trials are required to obtain a moderate character recognition accuracy.…”

Section: P300 Speller Paradigmmentioning

confidence: 99%

Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer Interface

Liu

Wang

2016

IEICE Trans. Inf. & Syst.

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SUMMARYA brain-computer interface (BCI) translates the brain activity into commands to control external devices. P300 speller based character recognition is an important kind of application system in BCI. In this paper, we propose a framework to integrate channel correlation analysis into P300 detection. This work is distinguished by two key contributions. First, a coefficient matrix is introduced and constructed for multiple channels with the elements indicating channel correlations. Agglomerative clustering is applied to group correlated channels. Second, the statistics of central tendency are used to fuse the information of correlated channels and generate virtual channels. The generated virtual channels can extend the EEG signals and lift up the signal-to-noise ratio. The correlated features from virtual channels are combined with original signals for classification and the outputs of discriminative classifier are used to determine the characters for spelling. Experimental results prove the effectiveness and efficiency of the channel correlation analysis based framework. Compared with the state-of-the-art, the recognition rate was increased by both 6% with 5 and 10 epochs by the proposed framework. key words: brain-computer interface (BCI), channel correlation analysis, virtual channels, P300 speller paradigm

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Section: P300 Speller Paradigmmentioning

confidence: 99%

Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer Interface

Liu

Wang

2016

IEICE Trans. Inf. & Syst.

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“…The collected EEG signals were further low-pass filtered by a 20-sample moving average filter with a corresponding cut-off frequency of 12.8 Hz to minimize possible muscle artifacts, while still preserving the P300 potential [23]. The VR classroom training system gave rise to eye movements from the subjects.…”

Section: Classifiermentioning

confidence: 99%

BCI inside a virtual reality classroom: a potential training tool for attention

Rohani

Puthusserypady

2015

EPJ Nonlinear Biomed Phys

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Background: A growing population is diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and are currently being treated with psychostimulants. Brain Computer Interface (BCI) is a method of communicating with an external program or device based on measured electrical signals from the brain. A particular brain signal, the P300 potential, can be measured about 300 ms after a voluntary cognitive involvement to external stimuli. By utilizing the P300 potential, we have designed a BCI-assisted exercising tool targeting attention enhancement within an immersive 3D virtual reality (VR) classroom. Methods: Combining a low-cost infrared camera with an "off-axis perspective projection" algorithm to achieve the illusion of 3D, an engaging training environment has been created. The setup also includes a single measurement electrode placed on the scalp above the parietal lobe (Pz). Two sets of experiments have been performed to elicit the P300 potential. One used a system which is a variant of Farwell and Donchin's famous P300 speller and the other used a system where the user is required to search for a specific letter in a series of changing images. A non-linear optimized support vector machine (SVM) classifier has been used to automatically detect the P300 potential. Results: Six subjects have participated in the preliminary experiment to test the prototype system, and an average error rate below 0.30 have been achieved, which is noteworthy considering the simplicity of the scheme. Conclusions:This work has successfully demonstrated a non-intrusive, low-cost, and portable system targeting attention in a motivating and engaging environment.

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“…Sci. 2016, 6, 142 2 of 19 emotion recognition [16,17], and steady-state visually evoked potential (SSVEP)-based BCIs [18,19]. In this paper, EEG is adopted as the input of the developed BCI.…”

Section: Introductionmentioning

confidence: 99%

“…However, a long-term continuous flickering stimulus may result in visual fatigue. Therefore, SSVEP is useful in designing a brain-controlled switch [18]. In contrast with SSVEP, P300 is relatively suitable for the design of a function BCI with multiple options.…”

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 Asynchronous P300 BCI With SSVEP-Based Control State Detection

Cited by 183 publications

References 34 publications

Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer Interface

Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer Interface

BCI inside a virtual reality classroom: a potential training tool for attention

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

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