Blind Framework With Low Complexity Model Order Selection and Unsupervised Identification of Visually Evoked Potential Components

Visual evoked potential (VEP) plays a crucial role in the diagnosis of nerve diseases and epilepsy. By applying luminous stimulation in different frequencies, neuronal electrical voltage changes are measured in the visual cortex area at the rear of the head using magnetoencephalogram (MEG). Such acquired MEG signals are separated into components using blind source separation (BSS). The MEG measurements are analyzed by medical experts and, based on their subjective interpretation, the VEP components are identified. Supervised machine learning (ML) methods can be applied to identify VEP components. However, these methods require labeled data, which must be generated through the subjective interpretation of medical experts. This can be limiting as medical experts traditionally assume a fixed amount of components. This paper proposes a blind framework to estimate the model order of the MEG measurements and to extract the VEP components. In order to estimate the amount of components, the framework exploits a low computational complexity modified Akaike Information Criterion (AIC) and does not require human intervention. In order to overcome the need for labeled data, we propose three approaches to automatically compare components extracted from MEG measurements with and without stimulation. Since each proposed unsupervised identification approach identifies a set of VEP components, we propose their decision fusion using set operations. The proposed framework does not require any human intervention, and it can be used as a complementary tool to support experts in identifying VEP components. The results are presented in terms of average amplitude spectrum and spectral topography. The automated procedure employing the combined unsupervised identification approaches is capable of identifying up to five, eight, and seven VEP components at stimulation frequencies of 3.84 Hz, 21.73 Hz, and 19.23 Hz, respectively. Moreover, upon intersecting with the independent components identified through visual inspection, this count decreases to two, four, and two VEP components for the stimulation frequencies 3.84 Hz, 21.73 Hz and 19.23 Hz, respectively. Finally, the calibration parameter of the unsupervised identification approaches can be adapted in order to select a greater or smaller amount of components. The proposed framework is validated using real MEG measurements.