Alpha and beta EEG brainwave signal classification technique: A conceptual study

Zainuddin, Balkis Solehah; Hussain, Z.; Isa, Iza Sazanita

doi:10.1109/cspa.2014.6805755

Cited by 21 publications

(1 citation statement)

References 22 publications

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“…For the proposed real-time BCI system, a simple signal processing and decision-making algorithm for visual attention and motor intention detection using Welch's periodogram method algorithm was performed for fast computations [ 29 , 30 ]. Calibration: Before using the proposed system, baseline parameters were collected while the user looked at a blank screen for 4 s five times.…”

Section: The Real-time Illusory Visual Motion Stimulus-based Bci Systmentioning

confidence: 99%

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

Punsawad

Siribunyaphat

Wongsawat

2021

Heliyon

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This paper presents an illusory visual motion stimulus-based brain-computer interface (BCI). We aim to use the proposed system to enhance the motor imagery (MI) modality. Since motor imagery requires a long time for training, a stimulation method with external stimuli through the sensory system is an alternative method for increasing efficiency. The research is divided into two parts. First, we observed the visual motion illusion pattern based on brain topographic maps for the novel BCI modality. Second, we implemented the illusory visual motion stimulus-based BCI system. Arrow and moving-arrow patterns were used to modulate alpha rhythms at the visual and motor cortex. The arrow pattern had an average classification accuracy of approximately 78.5%. Additionally, illusory visual motion stimulus-based BCI systems are proposed using the proposed feature extraction and decision-making algorithm. This proposed BCI system can control the cursor moving in the left or right direction with the designed algorithm to create five commands for assistive communication. Ten volunteers participated in the experiment, and a brain-computer interface system with motor imagery and an illusory visual motion stimulus were used to compare efficiencies. The results showed that the proposed method achieved approximately 4% higher accuracy than motor imagery. The accuracy of the proposed illusory visual motion stimulus and algorithm was approximately 80.3%. Therefore, an illusory visual motion stimulus hybrid BCI system can be incorporated into the MI-based BCI system for beginner motor imagery. Based on the results, the proposed assistive communication system can be used to enhance communication in people with severe disabilities.

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Section: The Real-time Illusory Visual Motion Stimulus-based Bci Systmentioning

confidence: 99%

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

Punsawad

Siribunyaphat

Wongsawat

2021

Heliyon

View full text Add to dashboard Cite

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A Proposal of an Empirical Methodology to Approximate an Electroencephalographic Signal with Appropriate Representatives of the Fourier Transformation

Zavaleta-Viveros

Toledo

Avendaño-Garrido

et al. 2020

Advances in Computational Intelligence

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An IoT Model to Improve Cognitive Skills of Student Learning Experience Using Neurosensors

Padhi

Babu

Jha

et al. 2018

Internet of Things and Personalized Healthcare Systems

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Alpha and beta EEG brainwave signal classification technique: A conceptual study

Cited by 21 publications

References 22 publications

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

A Proposal of an Empirical Methodology to Approximate an Electroencephalographic Signal with Appropriate Representatives of the Fourier Transformation

An IoT Model to Improve Cognitive Skills of Student Learning Experience Using Neurosensors

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