The Effect of Duty Cycle and Brightness Variation of Visual Stimuli on SSVEP in Brain Computer Interface Systems

Oralhan, Zeki; Tokmakçı, Mahmut

doi:10.1080/03772063.2016.1176543

Cited by 14 publications

(13 citation statements)

References 39 publications

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“…However, recent advances in SSVEP-based BCIs have developed techniques and algorithms to overcome these obstacles. Several studies have proposed different stimulus design techniques to realize large number of targets with only few frequencies, but they have also suffered limitations [21,38,43,50,57,58,90]. Furthermore, a few researchers combined SSVEPs with P300 to generate more targets with less frequencies, but this was at the cost of task complexity, which eventually affected the performance of the system.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, it has also been shown that low-frequency stimuli can cause photosensitivity-based epileptic seizures and high-levels of visual fatigue and discomfort, especially when presented with large number of targets and longer periods [21,43]. Many studies have used high-frequency stimuli and variable duty cycles to reduce visual fatigue and discomfort but at the cost of a decrease in the performance of SSVEP-based BCIs [21,43,[56][57][58]. A few authors have also combined other EEG signals, e.g., P300, with SSVEP to overcome these limitations [22,[59][60][61][62][63].…”

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A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Mannan

Kamran

Kang

et al. 2020

Sensors

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Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Mannan

Kamran

Kang

et al. 2020

Sensors

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“…measurement. Because EEG has strong signal response, high signal to noise ratio (SNR), and practical usage [6]. Moreover, EEG measurement is a non-invasive method.…”

Section: Introductionmentioning

confidence: 99%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

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P300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants ''A'' and ''B'' for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset. INDEX TERMS Brain computer interface, deep Learning, human machine systems, P300 detection.

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“…Sistemin bütünündeki doğruluk oranı ise; doğru karakter seçimi ADAA algoritmasına göre yüzdesi olarak hesaplanmıştır 4. farklı heceletici modundaki deneyler sounucunda bütün katılımcıların 45 karakterlik test oturumundaki doğruluk oranı, bir dizideki en yüksek doğruluğa sahip tur sayısı ile hesaplanmıştır.…”

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P300 Tabanlı Beyin Bilgisayar Arayüzü Sistemlerinde Uyaranlar Arası Sürenin ve Uyaran Yapısının Performansa Etkisi

Oralhan¹

2019

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Elektroensefalografi temelli beyin bilgisayar arayüzü sistemleri kas sistemini kullanamayan hastalar için dış dünya ile iletişimini mümkün kılmaktadır. EEG temelli beyin bilgisayar arayüzü sistemleri için çeşitli beyin sinyal aktiviteleri kullanılmaktadır. Olay odaklı potansiyellerden bir tanesi olan P300 sinyali beyin bilgisayar arayüzü sistemleri için elverişli bir beyin sinyalidir. P300 tabanlı bir beyin bilgsayar arayüzü için en önemli performans parametrelerinden birisi sınıflandırma doğruluk oranıdır. Bu çalışmada satır sütun temelli P300 heceletici yapısındaki değişiklikle daha yüksek doğruluk oranı ile elde edilmesi hedeflenmiştir. P300 heceleticisi matris yapısında ve uyaranların aralık süreleri üzerinde değişiklikler yapılmıştır. Bundan önceki bir çok çalışma P300 heceletici yapısındaki uyaran renk ve biçimlerindeki değişiklikler ile yapılmıştır. Uyaran yapısı ve uyaran aralık sürelerindeki değişikleri kıyaslayıcı P300 heceleticileri ile ilgili çalışmalar yeterli seviyede değildir. Bu çalışmada dört farklı yapıdaki satır sütun bazlı P300 heceletici kullanılarak deneyler yapılmıştır. Deneyler ile toplanan EEG kayıtları ön işlemden geçirildikten sonra adımsal doğrusal ayrışım analizi ile sınıflandırılmıştır. Sınıflandırma neticesinde bu çalışmada karşılaştırılan heceleticilerden; 4x4 satır sütın bazlı P300 heceleticinin 150 ms uyaran aralık süresine sahip olan yapıdaki formu, ortalama doğruluk oranı %84,76 ile en yüksek olarak tespit edilmiştir. En düşük performans ise; 6x6 satır sütın bazlı P300 heceleticinin 300 ms uyaranlar arası geçiş süresine sahip modunda %50,48 olarak gözlenmiştir. Bu çalışma, satır sütun bazlı P300 heceleticisinin uyaran matris yapısındaki değişikliği ve farklı uyaran aralık sürelerinde yapılan deneylerle yüksek doğruluk oranı ile elde edilebileceğini göstermiştir.

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The Effect of Duty Cycle and Brightness Variation of Visual Stimuli on SSVEP in Brain Computer Interface Systems

Cited by 14 publications

References 39 publications

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

3D Input Convolutional Neural Networks for P300 Signal Detection

P300 Tabanlı Beyin Bilgisayar Arayüzü Sistemlerinde Uyaranlar Arası Sürenin ve Uyaran Yapısının Performansa Etkisi

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