Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
maryam farhadnia; Sepideh Hajipour; mohammad mikaili
Volume 17, Issue 1 , May 2023, , Pages 1-10
Abstract
Today, usage of brain-computer interface systems based on steady-state visual evoked potentials (SSVEPs) has been increased due to some advantages such as acceptable accuracy and minimal need for user training. Steady-state visual potentials are one of the most important patterns used in BCI systems, ...
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Today, usage of brain-computer interface systems based on steady-state visual evoked potentials (SSVEPs) has been increased due to some advantages such as acceptable accuracy and minimal need for user training. Steady-state visual potentials are one of the most important patterns used in BCI systems, which are generated in the occipital region of the brain by visual stimulation between 6 and 60 Hz. One of the effective methods for extracting the SSVEP frequency in BCI systems is called the Multiway Correlation Coefficient Analysis (MCCA) method, which is a tensorized version of the classical Correlation Coefficient Analysis (CCA) method and is based on multidimensional data.In this paper, inspired by the MCCA method, two new algorithms (PARAFAC-CCA and C-PARAFAC-CCA) have been proposed using the combination of CCA and PARAFAC decomposition. The purpose of the proposed algorithms is to improve the initial reference signal and achieve higher accuracy in SSVEP frequency detection in BCI systems. In the PARAFAC-CCA algorithm, after performing the PARAFAC decomposition on the multidimensional training data and obtaining the time component, the CCA method is implemented between the obtained time component and the sine-cosine reference signal, and the optimal reference signal is made from its output. Finally, the MLR algorithm is used between the EEG test data and the optimal reference signal in order to achieve the target frequency. The general steps of the C-PARAFAC-CCA algorithm are also similar to PARAFAC-CCA, with the difference that in the calculation of the time component, constrained PARAFAC is used in such a way that in each step of the ALS algorithm, CCA is applied once and the time component is improved. The efficiency of the proposed algorithms was investigated on the real data set and it was shown that compared to the MCCA method, the proposed algorithms have reached a higher average accuracy.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Faride Ebrahimi; Mohammad Mikaili
Volume 4, Issue 2 , June 2010, , Pages 97-108
Abstract
Different biological signals including EEG, EOG, and EMG are recorded in sleep labs to diagnose sleep disorders. Data recorded during sleep is usually analyzed by sleep specialists visually. Since the sleep data is usually recorded for a long time period- namely a whole night- its visual inspection and ...
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Different biological signals including EEG, EOG, and EMG are recorded in sleep labs to diagnose sleep disorders. Data recorded during sleep is usually analyzed by sleep specialists visually. Since the sleep data is usually recorded for a long time period- namely a whole night- its visual inspection and classification is a very demanding and time consuming task so automatic analysis can definitely facilitate that. The key to automatic sleep staging is to extract suitable features. In the current study two classes of features are extracted from EEG signal. The first group is the features calculated from the coefficients of wavelet packet transformation (WPT) and the second group consists of a number of frequency features and a time feature, the amplitude of EEG signal itself. These two sets of features were separately mapped on a two dimensional space by SOM neural networks. The mappings indicated that these features are highly discriminative in separating sleep stages automatically. The data extracted from awake and deep sleep EEGs were mapped on two totally different regions. The mapping also indicated that EEG signal is not enough to separate stages thoroughly, as extracted data from EEG during REM and the first stage of NREM are mapped on the same region. Data extracted from EEG signals in the second stage overlapped with other stages which are in agreement with physiological definition of sleep stages.
