On P300 Detection using Scalar Products

Fira, Monica; Goraș, Liviu; Lazar, Anca Mihaela

doi:10.14569/ijacsa.2018.090113

Cited by 2 publications

(2 citation statements)

References 9 publications

(11 reference statements)

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“…User interface development made use of EEG has been developed to validate design acceptance (Bang et al, 2011). P300 as brain wave reader devices are effective and efficient increasingly for use for HCI development because from brain waves, using that can know the user's reaction immediately for to a design of user interface (Zickler et al, 2013) and more specific for Brain-Computer Interaction (BCI) (Wasim et al, 2018), (Fira et al, 2018). Human personality is associated with visual design style; the suitability of the design with the visual design requirements to be examined by arranging the position of an object at the display (Ziemkiewicz et al, 2012).…”

Section: Study Literaturementioning

confidence: 99%

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Perdana¹,

Santosa²,

Setiawan³

et al. 2021

Journal of Computer Science

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After obtaining the consistency of Visual Evoked Potential (VEP) by using a stimulus with data-entry user interface design. This study tries to confirm whether an individual's VEP will be classified according to personality type, which results from the proceeds of conventional personality tests usually carried out by the organization that is the place of this research. The results of this study will be an attempt to complement the needs of particular organizations for the elicitation stage to show user personas by using personality types as something to consider in software development in organizations. The recording of 20 participants whose personality types were known from the results of personality tests at the organization was completed. Each individual has a unique VEP. to confirm personality types and it is necessary to prove the similarity of VEP between individuals of the same type. 93.75% of VEP is classified according to personality type. Therefore it is necessary to consider that personality type can be used as user persona for user classification at the stage of user needs. To identify personality types at the personality test stage, it is still necessary to add more participants involved to get more convincing results.

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Section: Study Literaturementioning

confidence: 99%

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Perdana¹,

Santosa²,

Setiawan³

et al. 2021

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…The finer temporal resolution of EEG signal benefits scientific arena to exploit it in research and engineering. Recent developments [1][2][3][4][5][6][7] prove that different human activities can be recognized or classified by the EEG signal. Besides vast applications and implementations of EEG signal in the various fields (biomedical instrumentation, neuroscience, brain-computer interface, etc.…”

Section: Introductionmentioning

confidence: 99%

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Rahman¹,

Rashid²,

Khanam³

et al. 2019

ijacsa

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Since several work requires continuous alertness like efficient driving, learning, etc. efficient measurement of the alertness states through neural activity is a crucial challenge for the researchers. This work reports a practical method to investigate the alertness state from electroencephalography (EEG) of the human brain. Here, we have proposed a novel idea to monitor the brain alertness from EEG signal that can discriminate the alertness state comparing resting state with a simple statistical threshold. We have investigated two different types of mental tasks: alphabet counting & virtual driving to monitor their alertness level. The EEG signals are acquired from several participants regarding alphabet counting and virtual motor driving tasks. A 9-channel wireless EEG system has been used to acquire their EEG signals from frontal, central, and parietal lobe of the brain. With suitable preprocessing, signal dimensions are reduced by principal component analysis and the features of the signals are extracted by the discrete wavelet transformation method. Using the features, alertness states are classified using the artificial neural network. Additionally, the relative power of responsible frequency band to alertness is analyzed with statistical inference. We have found that the beta relative power increases at a significant level due to alertness which is good enough to differentiate the alertness state from the control state. It is also found that the increment of beta relative power for virtual driving is much greater than the alphabet counting mental alertness. We hope that this work will be very helpful to monitor constant alertness for efficient driving and learning.

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On P300 Detection using Scalar Products

Cited by 2 publications

References 9 publications

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

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