Artan teknoloji, düşen maliyetler ve küçülen donanım boyutları nedeniyle işaret işleme yöntemleri birçok alanda sıklıkla kullanılmaya başlanmıştır. Algılanan bazı sinyaller tek bir kaynaktan değil de birçok kaynaktan oluşan sinyallerin karışımı olabilmektedir. Bu tip durumlarda işaret işleme teknikleriyle elde edilebilecek başarı oranı düşüktür. Aynı zamanda sistemin içyapısının anlaşılması zordur. Bu tip durumlarda gizli kaynak ayrıştırma işlemi ile ölçülen sinyaller gizli kaynaklarına ayrıştırılabilmektedir. Bu amaçla yaygın olarak kullanılan temel kaynak ayrıştırma (PCA) ve bağımsız kaynak ayrıştırma (ICA) istatiksel yöntemlerinde sinyallerin ayrıştırılabileceği gizli kaynak sayısı ölçülen sinyal sayısı ile sınırlıdır. Bu sebeple karmaşık sinyallerde gizli kaynaklara ulaşmak bu yöntemlerle zordur. Bu yöntemlere alternatif olarak yapay sinir ağları (YSA) da gizli kaynak ayrıştırma amacıyla başarıyla kullanılmıştır. Bu çalışmada ise tek gizli katmanlı ileri beslemeli yapay sinir ağlarını eğitmek için kullanılan aşırı öğrenme makineleri (ELM) yöntemi klasik YSA ile gizli kaynak ayrıştırma yöntemlerinden farklı bit yaklaşım ile kullanılarak tek bir sinyal birden fazla birbirinden bağımsız gizli kaynağa ayrıştırılmıştır. Bu amaçla EEG, EMG, ECG sinyalleri ile ivmeölçer, magnetometre ve jiroskop algılayıcılarından alınan zaman sinyalleri gizli kaynaklarına ayrıştırılmıştır. Önerilen metodun başarısını eğitim başarısını gösteren ortalama hataların karekökü (RMSE) ve gizli kaynakların bağımsızlığını gösteren kovaryans kullanılmıştır. Test sinyallerinde 10-4 -64x10-4 aralığında RMSE ve 0.2x10-4 -38.3x10-4 aralığında kovaryans değerleri elde edilmiştir. Elde edilen RMSE değerleri YSA’nın başarılı olarak eğitildiğini, elde edilen kovaryans değerleri ise ayrıştırılmış sinyallerin birbirinden bağımsız olduğunu göstermiştir. Ayrıca 2 adet epileptik ve 2 adet normal EEG sinyali 16 ayrı gizli kaynağa kadar ayrıştırılmıştır. Dört örnekte de elde edilen başarı oranları önerilen metodun gizli kaynak ayrıştırmada başarıyla kullanılabileceğini göstermiştir.

Improvements in the technology cause not only decrease in the sensor costs but also, reduce the dimensions of the sensors. Based on these facts the importance of signal processing methods increases day by day. But, in most applications sensors pick up a collection or mixture of signals from many sources instead of only one sensor. In this case, it is hard to understand or manipulate the system. For example, recorded EEG signals are mixtures of action potentials of many neurons, therefore, the reasons or mechanisms behind epilepsy are still since unknown. For such situations, blind signal separation methods have been employed. Generally, used blind signal separation methods are principal component analysis (PCA) and independent component analysis (ICA) methods. Although many successful results of PCA and ICA have been reported in the literature, they suffer from a major drawback; the number of sources that will be separated must be equal or less than the number of sensors. Therefore, it is hard to obtain the true sources of complex signals by these methods. As an alternative, the method of delay was proposed to separate a signal into two independent sources, but its performance is highly dependent on the delay value. Addition to these methods, artificial neural network (ANN) was also employed for the purpose of blind signal separation. By ANN, better blind signal separation results with compared to PCA and ICA were reported in the literature. In this study, extreme learning machine, which is a novel learning scheme of single hidden layer feed-forward artificial neural network, was employed. In ELM, the weights and biases in the hidden layer are assigned arbitrary and the weights in the output layer are calculated analytically. Therefore, ELM showed faster training stage and higher generalization capability with compare to back-propagation trained ANN. The proposed approach has three main contributions, which are: (1) proposed approach can be employed to separate a signal into a desired number of sources without the limitation of PCA and ICA, which is the number of sources that the signals can be separated, must be equal to or less than the observations, (2) unlike the classical ANN approaches, in the proposed approach, an orthogonal transfer function was used in the output of each neuron in the hidden layer, and (3) the proposed approach is extremely fast because of the training scheme of ELM. To evaluate and validate the proposed approach, biological signals, which are the EEG, ECG, EMG, accelerometer, gyroscope, and magnetometer signals, were utilized. Achieved RMSE in the training stage were in the range of 10-4 -64x10-4 and the duration of training for different lengths of data were in the range of 0.37-2.33 sec, for separating a signal into 2 and 10 sources, respectively, while the length of the dataset is 106 . Additionally, obtained covariance of the separated signals were in the range of 0.2x10-4 -38.3x10-4 . Obtained RMSE values and fast training stage showed that the proposed approach has high accuracy in the separation of a signal and also it has an extremely fast learning stage. These advantages are because of the training scheme of ELM. Moreover, obtained covariance values showed that the separated signals are highly independent. In order to investigate the reason of obtained low covariance, a traditional transfer function was employed and not only higher covariance but also higher RMSEs were obtained. These results showed that using orthogonal transfer functions (such as sine and cosine, Bessel functions, etc.) after each neuron in the hidden layer increases the dependency of the separated signals. Furthermore, two normal EEG and two epileptic EEG signals were separated into 1-16 independent sources. Achieved results showed that there is not any relation between the numbers of separated sources with obtained the independency of sources of sources and also training accuracy. As a summary, in this study a novel approach, which is based on ELM, was proposed in order to separate a signal into a desired number of independent sources. Achieved results showed that the proposed approach is fast and can successfully separate a signal into independent sources.