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Deep neural network training with iPSO algorithm [IPSO algoritmasi ile derin sinir agi egitimi]
(Institute of Electrical and Electronics Engineers Inc., 2018)
Deep learning-based methods are frequently preferred in many areas in recent years. Another issue, which is as important as deep neural networks applications, is the training of deep neural networks. Although many techniques ...
Design of a nonlinear adaptive infinite impulse response filter [Dogrusal olmayan sonsuz darbe cevapli adaptif filtre tasarimi]
(2007)
This study introduces a wavelet network based adaptive IIR filtering system satisfying asymptotic stability in the sense of Lyapunov. The proposed system is also integrated with the advantages of the time-frequency specific ...
Wavelet denoising of middle latency response in auditory evoked potentials
(2011)
This paper presents a new approach for obtaining Middle Latency Response (MLR) in auditory evoked potentials. In this study, we first generated synthetic single trial MLR data at some specified noise levels by using ...
An adaptive noise canceller based on QLMS algorithm for removing EOG artifacts in EEG recordings
(Institute of Electrical and Electronics Engineers Inc., 2017)
In this paper, a novel adaptive noise canceller (ANC) based on the quaternion valued least mean square algorithm (QLMS) is designed in order to remove electrooculography (EOG) artifacts from electroencephalography (EEG) ...
Complex-valued least mean Kurtosis adaptive filter algorithm [Kompleks-Degerli En Küçük Ortalama Kurtosis Adaptif Filtre Algoritmasi]
(Institute of Electrical and Electronics Engineers Inc., 2016)
In this study, a complex-valued least mean Kurtosis (CLMK) adaptive filter algorithm is designed for processing complex-valued signals. The performance of the designed algorithm is tested on a complex-valued system ...
Automatic removal of ocular artefacts in EEG signal by using independent component analysis and Chauvenet criterion [Baglmslz bileşen analizi ve chauvenet kriteri kullanarak EEG sinyallerindeki oktiler artefaktlan otomatik yok etme]
(Institute of Electrical and Electronics Engineers Inc., 2017)
Eye movements (saccade, blink and etc.) cause artefacts in Electroencephalogram recordings. The ocular artefact can distort the EEG signals. Removal of ocular artefact is important issue in EEG signal analysis. The main ...
Automatic removal of ocular artefacts in EEG signal by using independent component analysis and artificial neural network
(Institute of Electrical and Electronics Engineers Inc., 2017)
Ocular artefacts caused by eye movements can distort Electroencephalogram (EEG) recordings. It is important to obtain clean EEG signals in diagnosing and interpreting diseases. Meaningful EEG signals should not be distorted ...
