Wavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals, however, there exists no evaluation for Steady-State Visually-Evoked Potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore if any, the mother wavelet that suits best to represent SSVEP signals for classification purposes in BCIs. In this study, three common wavelet-based features (variance, energy, and entropy) extracted from SSVEP signals for five distinct EEG frequency bands (delta, theta, alpha, beta, and gamma) were classified to determine three different user commands using six fundamental classifier algorithms. The study was repeated for six different commonly-used mother wavelet functions (Haar, Daubechies, Symlet, Coiflet, Biorthogonal, and Reverse Biorthogonal). The best discrimination was obtained as the accuracy of 100% with the average of 75.85%. Besides, Ensemble Learner gives the highest accuracies for half of the trials. Haar wavelet had the best performance in representing SSVEP signals among other all mother wavelets adopted in this study. Concomitantly, all three features of energy, variance, and entropy should be used together since none of these features had superior classifier performance alone.
Brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) have been acceleratingly used in different application areas from entertainment to rehabilitation, like clinical neuroscience, cognitive, and use of engineering researches. Of various electroencephalography paradigms, SSVEP-based BCI systems enable apoplectic people to communicate with outside world easily, due to their simple system structure, short or no training time, high temporal resolution, high information transfer rate, and affordable by comparing to other methods. SSVEP-based BCIs use multiple visual stimuli flickering at different frequencies to generate distinct commands. In this paper, we compared the classifier performances of combinations of binary commands flickering at seven different frequencies to determine which frequency pair gives the highest performance using temporal and spectral methods. For SSVEP frequency recognition, in total 25 temporal change characteristics of the signals and 15 frequency-based feature vectors extracted from the SSVEP signal. These feature vectors were applied to the input of seven well-known machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbour, and Ensemble Learning). In conclusion, we achieved 100% accuracy in 7.5 - 10 frequency pairs among these 2,520 distinct runs and we found that the most successful classifier is the Ensemble Learning classifier. The combination of these methods leads to an appropriate detailed and comparative analysis that represents the robustness and effectiveness of classical approaches.
Eight distinct stimulation frequencies can be discriminated with the average of 36.1%. Reverse Biorthogonal mother wavelet gives the maximum accuracy. Ensemble learners give maximum accuracy among classifiers Figure A. Block diagram of the study Purpose: This study focuses on determining the stimulating frequency of a flashing image on the computer screen via electroencephalography (EEG) signals to investigate whether there is a correlation between stimulation frequency and brain activities. Theory and Methods: Some researches indicate an increase in classifier performances for brain-computer interfaces (BCIs) when objects are shown as blinking on the screen. The recorded EEG signals while applying a blinking image on a screen are called steady-state visually-evoked potentials (SSVEP). Most of the studies tried to discriminate which object is gazed while recording. We tried to determine the stimulating frequency from this dataset using an open-source SSVEP dataset. Wavelet features of variance, energy, and entropy were calculated from six different mother wavelets (Haar, Daubechies, Symlet, Coiflet, Biorthogonal, Reverse Biorthogonal). All features and ANOVA-selected features were applied to distinct classifiers (Decision Trees, Discriminant Analysis, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, and Ensemble Learners). Results: The mother wavelet of Reverse Biorthogonal gives the maximum accuracy by using all classifiers. The ensemble learner determines the stimulating frequency correctly with a maximum accuracy of 37.9%. On the other hand, ANOVA-based feature selection decreases the classifier performances for all subjects. Conclusion: Results indicate that the stimulating frequency is embedded into SSVEP signals. We concluded that SSVEPbased BCI studies for object classification or decision making should be conducted carefully since not only the location of the blinking object but also its blinking frequency is the part of EEG signals. In conclusion, it will be possible to detect gazed object among a few pictures by showing the flickering objects at the different frequencies at the same time.
Beyin-Bilgisayar Arayüzü (BBA), mevcut kas ve sinir sistemlerini çeşitli nedenlerle kontrol edemeyen bireylerin dış dünya ile etkileşime girmelerini sağlayan bir iletişim sistemidir. Temel olarak, bir BBA, kullanıcının beyin aktiviteleri sırasında üretilen sinyalleri işleyerek bazı elektronik cihazlarla iletişim kurmasını sağlar. Bu çalışma, sınıflandırma yoluyla Elektroensafalogram (EEG) sinyalleri içindeki sabit bakış verilerini belirlemeye ve toplamaya çalışmaktadır. Bu amaçla Autonomous Üniversitesi'ndeki araştırmacılar tarafından kaydedilen EEG sinyallerinden oluşan üç veri seti incelenmiştir. Bu veri kümelerindeki EEG sinyalleri, deneklerin bilgisayar ekranında gösterilen beş kutuya bakışlarının Durağan Durum Görsel Uyarılmış Potansiyel bazlı BBA ile tanındığı bir ortamda toplanmıştır. Naive Bayes, Aşırı Öğrenme Makinesi ve Destek Vektör Makineleri algoritmaları kullanılarak sınıflandırma yapıldı. EEG sinyallerinden Özbağlanımlı, Hjorth ve Güç Spektral Yoğunluğu olarak üç öznitelik seti çıkarılmıştır. Sonuç olarak, Özbağlanımlı özniteliklerin kullanıldığı durumda sınıflandırıcılar %45.67 ile %78.34 arasında performans gösterirken, Hjorth özniteliği kullanıldığında sınıflandırma performansları %43.34-75.25 ve son olarak Güç Spektral Yoğunluğu kullanılarak sınıflandırma performansları %57.36 ile %83.42 arasındadır. Ayrıca sınıflandırma performansları, sınıflandırma algoritmalarına göre Naive Bayes için %52.23 ile 79.15, Aşırı Öğrenme Makinesi için %56.32-83.42 ve Destek Vektör Makineleri için %43.34-72.27 arasında değişmektedir. Elde edilen doğruluk performansları arasında en iyi doğruluk değeri, Güç Spektral Yoğunluk özniteliği ve Aşırı Öğrenme Makinesi algoritması çifti ile elde edilen %83.42 olmuştur.
Brain Computer Interface (BCI) is a system that enables people to communicate with the outside world and control various electronic devices by interpreting only brain activity (motor movement imagination, emotional state, any focused visual or auditory stimulus, etc.). The visual stimulation based recording is one of the most popular methods among various electroencephalography (EEG) recording methods. Steady-state visual-evoked potentials (SSVEPs) where visual objects are blinking at a fixed frequency play an important role due to their high signal-to-noise ratio and higher information transfer rate in BCI applications. However, the design of multiple (more than 3) commands systems in SSVEPs based BCI systems is limited. The different approaches are recommended to overcome these problems. In this study, an approach based on machine learning is proposed to determine stimulating frequency in SSVEP signals. The data set (AVI SSVEP Dataset) is obtained through open access from the internet for simulations. The dataset includes EEG signals that was recorded when subjects looked at a flickering frequency at seven different frequencies (6-6.5-7-7.5-8.2-9.3-10Hz). In the machine learning-based approach Wigner-Ville Distribution (WVD) is used and features are extracted using Time-Frequency (TF) representations of EEG signals. These features are classified by Decision Tree, Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes, Ensemble Learning classifiers. Simulation results demonstrate that the proposed approach achieved promising accuracy rates for 7 command SSVEP systems. As a consequence, the maximum accuracy is achieved in the Ensemble Learning classifier with 47.60%.
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