EEG-based Computer Control Interface for Brain-Machine Interaction

Katona, József; Kővári, Attila

doi:10.3991/ijoe.v11i6.5119

Cited by 35 publications

(12 citation statements)

References 21 publications

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“…The latest BCIs applied with biosensors and modern signal processing units have become cheaper and more portable because of their simple structure, while their accuracy is similar to that of the clinical EEG devices [10,31,32]. The research trends that deal with BCI systems include many other application areas of use, for instance, using BCI for observing the brain's procession of information [13][14][15][16] or implementing navigation tasks [17][18][19][20]. Beside these, the implementation of such a complex system and the observation of its efficiency can greatly be used in the field of education either among interested students or in teacher-managed projects.…”

Section: The Brain-computer Interfacementioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

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Brain research is one of the most significant research areas of the last decades, in which many developments and modern engineering technologies are applied. The electroencephalogram (EEG)-based brain activity observation processes are very promising and have been used in several engineering research fields. Objective: The main goal of this research was to develop a Brain-Computer Interface (BCI) system to observe the level of vigilance calculated by Think Gear-ASIC Module (TGAM1) technology and to evaluate the output with learning efficiency tests applied in cognitive neuroscience. Methods: The performance of the BCI system is evaluated in a comparative study. The BCI system was tested by thirty-two test subjects and the attention level output was compared by the Psychology Experiment Building Language's (PEBL's) Corsi block test (P CORSI) and PEBL's Ebbinghaus procedure (P EBBINGHAUS) tasks. Results: Using the BCI, we have shown statistically significant results between the BCI and the conventional cognitive neuroscience tests. The correlation between the tests and the average attention of the BCI was slightly strong for P CORSI Total Score (r=.63, p<.01 (2-tailed) and slightly strong for P EBBINGHAUS Total Correct (r=-.71, p<.01 (2-tailed). The average level of attention measured by the BCI system during the P CORSI test was 49.00%, while in case of the P EBBINGHAUS test it was 52.41% on all samples. Conclusion: The developed BCI system has a significant correlation with P CORSI and P EBBINGHAUS cognitive neuroscience tests. The BCI system is capable of observing attentional vigilance continuously. Significance: The developed BCI system is applicable to observe vigilance level in realtime while the level of attention depends on activities.

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Section: The Brain-computer Interfacementioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

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“…Our aim of this study is to develop the TMS navigation system for suppressing MEP fluctuation using online EEG measurements. We employed EEG coherence analysis to measure EEG stability using two channel electrodes [19]. EEG coherence analysis is generally used to measure the similarity of two or more signal waveforms in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

EEG Coherence Analysis for Suppression of MEP Amplitude Variability in TMS

2021

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Transcranial magnetic stimulation (TMS) is a non-invasive stimulation method for cortical neurons. When TMS is delivered to the primary motor cortex (M1), motor evoked potentials can be measured in electromyograms for the peripheral muscle. However, the motor-evoked potential (MEP) amplitudes measured by stimulations for M1 fluctuated from trial to trial. MEP fluctuations are caused by changes in cortical excitability. We hypothesized that MEP variability could be suppressed with application of TMS when cortical excitability was stable. Thus, we developed a TMS system to suppress MEP amplitude variabilities. We used electroencephalographic (EEG) online measurements with coherence analysis to obtain the similarity of cortical excitabilities. The system enables us to trigger TMS if the EEGs measured from the two channels have a high similarity in the frequency domain. In this study, we found that the suppression of MEP fluctuation was dependent on the state of cortical excitability obtained by EEG coherence analysis.

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“…Thus, this paper proposed the use of encephalography (EEG) physiological measurement for attention detection. The choice of EEG is because it is proven to be fast tool for instant examination for attention evaluations [1,[3][4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Mean Fuzzy Approach for Attention Detection

et al. 2021

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Statistics around the world showed that attention deficit significantly leads to road accidents. Hence, the growth of studies on attention deficit detection becoming more important. The studies obtained the waveform from electroencephalography (EEG) to identify the characteristic of attention. However, each individual has own unique characteristics to significantly shown the attention deficit. Thus, this research aim is to use the fuzzy approach to minimize the variability gap of the EEG signal between each individual. The research conducted the prior experiment to develop control parameter for training set of fuzzy by using two distinct stimulations to create two groups of attention sample i.e., attentive and inattentive. An approach of novel Hybrid Mean Fuzzy (HMF) was proposed in this research to detect attention deficit in EEG signal. It is the combination of simple averaging (Mean) and Fuzzy approaches for EEG analysis and classification. The results of using this method shows a significantly change in EEG signal which correlates to the attention detection. An Attention Degradation Scale (ADS) is successfully developed as the threshold value of EEG for attention detection. Therefore, the findings in this research can be a promising foundation on attention deficit detection in large application not only for reducing the road accidents.

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EEG-based Computer Control Interface for Brain-Machine Interaction

Cited by 35 publications

References 21 publications

Examining the Learning Efficiency by a Brain-Computer Interface System

Examining the Learning Efficiency by a Brain-Computer Interface System

EEG Coherence Analysis for Suppression of MEP Amplitude Variability in TMS

Hybrid Mean Fuzzy Approach for Attention Detection

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