Design of an online BCI system based on CCA detection method

Lin, Dongxue; Chuan, Tan Jeffrey Too; Zhu, Chi; Duan, Feng

doi:10.1109/chicc.2015.7260370

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…As regards the obtained accuracies in absolute terms, our results are in line with literature regarding multiclass SSVEP recognition with the standard CCA technique [ 7 , 14 , 20 , 26 , 32 ], although a subject-specific calibration of the stimulation frequencies and/or their duty cycles [ 33 ] could have further increased the performances. In addition, we verified that the combination of our proposed variations could produce the same accuracy increments as other CCA-related methods in literature and particularly the same improvements as filter bank CCA of Chen et al [ 26 ].…”

Section: Discussionsupporting

confidence: 88%

Sinc-Windowing and Multiple Correlation Coefficients Improve SSVEP Recognition Based on Canonical Correlation Analysis

Mondini

Mangia

Cappello

2018

Computational Intelligence and Neuroscience

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Canonical Correlation Analysis (CCA) is an increasingly used approach in the field of Steady-State Visually Evoked Potential (SSVEP) recognition. The efficacy of the method has been widely proven, and several variations have been proposed. However, most CCA variations tend to complicate the method, usually requiring additional user training or increasing computational load. Taking simple procedures and low computational costs may be, however, a relevant aspect, especially in view of low-cost and high-portability devices. In addition, it would be desirable that the proposed variations are as general and modular as possible to facilitate the translation of results to different algorithms and setups. In this work, we evaluated the impact of two simple, modular variations of the classical CCA method. The variations involved (i) the number of canonical correlations used for classification and (ii) the inclusion of a prefiltering step by means of sinc-windowing. We tested ten volunteers in a 4-class SSVEP setup. Both variations significantly improved classification accuracy when they were used separately or in conjunction and led to accuracy increments up to 7-8% on average and peak of 25–30%. Additionally, variations had no (variation (i)) or minimal (variation (ii)) impact on the number of algorithm steps required for each classification. Given the modular nature of the proposed variations and their positive impact on classification accuracy, they might be easily included in the design of CCA-based algorithms that are even different from ours.

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Section: Discussionsupporting

confidence: 88%

Sinc-Windowing and Multiple Correlation Coefficients Improve SSVEP Recognition Based on Canonical Correlation Analysis

Mondini

Mangia

Cappello

2018

Computational Intelligence and Neuroscience

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“…Though the efficiency of such a system is very good, user training is mandatory which may not be desirable in some cases. In another system, an online BCI system has been designed using the steady state visually evoked potential [6] to control the robotic arm [10]. The main drawback of this system is that a computer and the related software needs to be setup.…”

Section: Introductionmentioning

confidence: 99%

Home Automation Using Brain Signals

Mohan¹,

Mallikarjun²,

Beeresh³

et al. 2023

IJSRCSEIT

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In this time of digitization and computerization, the life of individuals is getting more straightforward as nearly everything is programmed. This interconnection of the things can be used to help people with physical disabilities including paralysis. The brain signals of such people can be harnessed to create a home automation system. In this work, the concentration levels of such individuals are extracted using the EEG signals. These signals are then used to control the electronic devices. The designed system has been tested to operate the switching of an electric bulb using brain signals. This system can also be extended to assist a physically impaired individual to have effective control over electrical and electronic appliances and devices within a home.

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