Browsing by Subject "Brain computer interface"

Now showing 1 - 3 of 3

Open Access
Effects of high stimulus presentation rate on EEG template characteristics and performance of c-VEP based BCIs
(IOP, 2019-03-28) Başaklar, Toygun; Tuncel, Yiğit; İder, Yusuf Ziya
Objective: This study aims at investigating the effects of high stimulus presentation rates on the characteristics of c-VEP responses along with the change in performance depending on the stimulus presentation rate by utilizing a c-VEP based speller BCI. Approach: Twenty subjects participated in three different experiments with refresh rates of 60 Hz (E1), 120 Hz (E2) and 240 Hz (E3), where a 127-bit m-sequence is used. To observe the change in frequency content of c-VEP responses, PSD estimates of c-VEP responses were evaluated. Principal component analysis (PCA) was applied to observe how many distinguishable responses could be evoked with a 127-bit length m-sequence for three different refresh rates. Main Results: Average ITR and accuracy values are 85.87 bits min−1 and 92% for E1, 94.21 bits min−1 and 97% for E2, and 78.65 bits min−1 and 87% for E3 respectively. The c-VEP responses are band-limited although the bandwidth of the input signal significantly increases as the refresh rate increases. The spectral densities of c-VEP templates are concentrated on several frequency intervals, especially for E3, which eventually results in a target misclassification. PCA shows that the number of well distinguishable responses decreases with the increasing refresh rate. Considering all results and observations, we suggest that 120 Hz refresh rate is best to use in BCIs with high number of targets whereas 240 Hz refresh rate may be prefered for low number of targets. Significance: This study mainly investigates the alterations in the characteristics of c-VEP responses according to the stimulus presentation rate which have never been investigated thoroughly before. Our results show that increasing refresh rate does not necessarily increase the overall performance of the system due to the changes in characteristics of c-VEP responses. Further applications and designs of a c-VEP based BCIs will benefit from the observations and results of this study.
Open Access
İki durumlu bir beyin bilgisayar arayüzünde özellik çıkarımı ve sınıflandırma
(IEEE, 2017-10) Altındiş, Fatih; Yılmaz, B.
Beyin bilgisayar arayüzü (BBA) teknolojisi motor nöronlarının özelliğini kaybeden ve hareket kabiliyeti kısıtlanmış ALS ve felçli hastalar gibi birçok kişinin dış dünya ile iletişimini sağlamaya yönelik kullanılmaktadır. Bu çalışmada, Avusturya’daki Graz Üniversitesi’nde alınmış EEG veri seti kullanılarak gerçek zamanlı EEG işleme simülasyonu ile motor hayal etme sınıflandırılması amaçlanmıştır. Bu veri setinde sağ el ya da sol elin hareket ettirilme hayali esnasında 8 kişiden alınmış iki kanallı EEG sinyalleri bulunmaktadır. Her katılımcıdan 60 sağ ve 60 sol olmak üzere toplamda 120 adet yaklaşık 9 saniyelik motor hayal etme deneme sinyali kayıt edilmiştir. Bu sinyaller filtrelemeye tabi tutulmuştur. Yirmi dört, 32 ve 40 elemanlı özellik vektörü bant geçiren filtreler kullanarak elde edilen göreceli güç değişim değerleridir (GGDD). Bu çalışmada, lineer diskriminant analizi (LDA), k en yakın komşular (KNN) ve destek vektör makinaları (SVM) ile sınıflandırma yapılmış, en iyi sınıflandırma performansının 24 değerli özellik vektörüyle ve LDA sınıflandırma yöntemiyle elde edildiği gösterilmiştir.
Open Access
New approach for designing cVEP BCI stimuli based on superposition of edge responses
(Institute of Physics Publishing, 2020-06) Yasinzai, Muhammad Nabi; İder, Yusuf Ziya
The purpose of this study is to develop a new methodology for designing stimulus sequences for Brain Computer Interfaces that utilize code modulated Visually Evoked Potentials (cVEP BCIs), based on experimental results regarding the behavior and the properties of the actual EEG responses of the visual system to binary-coded visual stimuli, such that training time is reduced and possible number of targets is increased. EEG from 8 occipital sites is recorded with 2000 sps, in response to visual stimuli presented on a computer monitor with 60 Hz refresh rate. EEG responses of the visual system to black-to-white and white-to-black transitions of a target area on the monitor are recorded for 500 ms, for 160 trials, and signal-averaged to obtain the onset (positive edge) and offset (negative edge) responses, respectively. It is found that both edge responses are delayed by 50 ms and wane completely within 350 ms. These edge responses are then used to generate (predict) the EEG responses to arbitrary binary stimulus sequences using the superposition principle. It is found that the generated and the measured EEG responses to certain (16) simple short sequences (16.67–350 ms) are highly correlated. These 'optimal short patterns' are then randomly combined to design the long (120 bit, 2 sec) 'Superposition Optimized Pulse (SOP)' sequences, and their EEG response templates are obtained by superposition of the edge responses. A SOP sequence-based Visual Speller BCI application yielded higher accuracy (95.9%) and Information Transfer Rate (ITR) (57.2 bpm), compared to when superposition principle is applied to conventional m-sequences and randomly generated sequences. Training for the BCI application involves only the acquisition of the edge responses and takes less than 4 min. This is the first study in which the EEG templates for cVEP BCI sequences are obtained by the superposition of edge responses.