In the feature design aspect, these studies have converted 1D EEG data into 2D image data in advance and classified the features via the deep network. However, the majority of these studies have focused on regular data, such as the same frequency and same length of the sample data. proposed a coding method for epileptic EEG signals based on the deep network. used an in-depth learning method based on a cloud platform to propose a solution for epilepsy prevention and control. converted the frequency bands extracted from brain waves into topographical maps (2D images) through spectral power and classified the images into depth networks. Tabar and Halici converted one-dimensional (1D) brain waves into two-dimensional (2D) image data through short-time Fourier transform and accessed the deep network for classification. Some researchers have studied EEG via a deep network. In-depth learning technology can accomplish numerous tasks that are difficult to complete in the traditional methods.
#NO EEG MANUAL#
Given this technology’s autonomous learning characteristics from data, it can directly skip the manual design features and extraction process in the traditional methods, avoid the difficulties of manual design features in traditional methods, and manually adjust numerous parameters. Hence, the universality of classification methods should be improved, while ensuring the enhanced detection and recognition of EEG data.Īt present, in-depth learning technology is a popular research area.
#NO EEG HOW TO#
This situation raises a question on how to improve the ability of classification methods to adapt to new data. The inconsistency of data specifications often affects the features obtained by traditional feature extraction methods.
#NO EEG PORTABLE#
However, although a variety of medical devices or portable EEG acquisition devices produce numerous EEG data that can be used for epilepsy research, the different data sources result in a lack of uniform data formats, such as different sampling frequencies, different signal lengths, and different sampling channels. For example, emotive has been widely used in brain-computer interface because it is lightweight and inexpensive and has similar performance to medical equipment. In addition, some portable EEG acquisition equipment has been developed. With the development of science and technology, the accuracy of medical EEG acquisition equipment has been improved. Moreover, some studies have combined or redesigned these methods to obtain new features, thereby eventually achieving good classification results. Numerous methods are used to extract EEG features, including time-domain, frequency-domain, and time-frequency analyses and chaotic features. Feature extraction from EEG data is one of the most important steps. Such a research often follows the following steps: EEG data acquisition and prepossessing, feature extraction, classification model training, and data prediction. With the development of computer science and technology, numerous studies have focused on the classification of features extracted from EEG signals by using a computer classification model. Since the 1980s, scholars have been conducting research on epilepsy based on electroencephalography (EEG), among which the identification of epilepsy by analyzing EEG data is one of the important research contents. Accordingly, brain wave analysis has become an effective and important method for the study of epilepsy.
It can record brain wave changes during brain activity and reflect the electrophysiological activities of the cerebral cortex or scalp surface of brain neurons. Brain wave is a synaptic postsynaptic potential generated by numerous neurons when the brain is active. Approximately 50 million epilepsy patients have been documented globally, and epilepsy has become one of the most common nervous system diseases endangering human health worldwide. IntroductionĮpilepsy is characterized by recurrent seizures caused by the abnormal discharge of brain neurons, which often bring physical and psychological harm to patients. However, the proposed depth network model showed considerably better universality and classification accuracy, particularly for EEG signals with short length, which was validated by two datasets. The artificial design feature extraction methods could not obtain stable classification results when analyzing EEG data with different sampling frequencies.
This study proposed a deep network model for autonomous learning and classification of EEG signals, which could self-adaptively classify EEG signals with different sampling frequencies and lengths. Thus, the adaptability of EEG classification methods has become significant.
The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the EEG data. In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals.