Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By analyzing the encephalogram (EEG) signals of patients with epilepsy, their conditions can be monitored and seizure can be detected and intervened in time. As the identification of effective features in EEG signals is important for accurate seizure detection, this paper proposes a multi-view deep feature extraction method in attempt to achieve this goal. The method first uses fast Fourier transform (FFT) and wavelet packet decomposition (WPD) to construct the initial multi-view features. Convolutional neural network (CNN) is then used to automatically learn deep features from the initial multi-view features, which reduces the dimensionality and obtain the features with better seizure identification ability. Furthermore, the multi- view Takagi-Sugeno-Kang fuzzy system (MV-TSK-FS), an interpretable rule-based classifier, is used to construct a classification model with strong generalizability based on the deep multi- view features obtained. Experimental studies show that the classification accuracy of the proposed multi-view deep feature extraction method is at least 1% higher than that of common feature extraction methods such as principal component analysis (PCA), FFT and WPD. The classification accuracy is also at least 4% higher than the average accuracy achieved with single-view deep features.

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic EEG signals are usually based on two assumptions: 1) adequate training examples are available for model training, and 2) the training set and the test set are sampled from datasets with the same distribution. Since seizures occur sporadically, training examples of seizures could be limited. Besides, the training and test sets are usually not sampled from the same distribution for generic non-patient-specific recognition of EEG signals. Hence, the two assumptions in traditional recognition methods could hardly be satisfied in practice, which results in degradation of model performance. Transfer learning is a feasible approach to tackle this issue attributed to its ability to effectively learn the knowledge from the related scenes (source domains) for model training in the current scene (target domain). Among the existing transfer learning methods for epileptic EEG recognition, transductive transfer learning fuzzy systems (TTL-FSs) exhibit distinctive advantages – the interpretability that is important for medical diagnosis and the transfer learning ability that is absent from traditional fuzzy systems. Nevertheless, the transfer learning ability of TTL-FSs is restricted to a certain extent since only the discrepancy in marginal distribution between the training data and test data is considered. In this study, the enhanced transductive transfer learning Takagi-Sugeno-Kang fuzzy system construction method (ETTL-TSK-FS) is proposed to overcome the challenge by introducing two novel transfer learning mechanisms: (1) joint-knowledge is adopted to reduce the discrepancy between the two domains, and (2) an iterative transfer learning procedure is introduced to enhance transfer learning ability. Extensive experiments have been carried out to evaluate the effectiveness of the proposed method in recognizing epileptic EEG signals on the Bonn and CHB-MIT EEG datasets. The results show that the method is superior to or at least competitive with some existing state-of-art methods under the scenario of transfer learning.

The intelligent recognition of electroencephalogram (EEG) signals has become an important approach to the detection of epilepsy. Among existing intelligent identification methods, fuzzy logic systems (FLSs) have shown a distinctive advantage in identifying epileptic EEG signals because of their strong learning abilities and interpretability. Like many conventional intelligent methods for recognizing EEG signals, in the training of FLS, it is assumed that the training dataset and test dataset are drawn from data that are identically distributed. However, this assumption is not necessarily valid in practice as it is not uncommon for the two datasets to have different distributions. To overcome this problem, a strategy is presented in this paper to construct a Takagi-Sugeno-Kang (TSK) FLS based on transductive transfer learning for identifying epileptic EEG signals. Two novel objective functions, achieved by integrating the transductive transfer learning mechanism, are proposed for the training of the TSK FLS. As regression and binary classification are two common approaches to multiclass classification, the TSK transfer learning FLS algorithms for regression and binary classification are developed, respectively, to construct the corresponding TSK FLS. Both algorithms are further used to perform a multiclass classification to recognize epileptic EEG signals. Their performance in the epileptic EEG datasets indicates promise in dealing with situations where the training and test datasets differ with regard to data distribution.

Electroencephalogram (EEG) signal identification based on intelligent models is an important means in epilepsy detection. In the recognition of epileptic EEG signals, traditional intelligent methods usually assume that the training dataset and testing dataset have the same distribution, and the data available for training are adequate. However, these two conditions cannot always be met in practice, which reduces the ability of the intelligent recognition model obtained in detecting epileptic EEG signals. To overcome this issue, an effective strategy is to introduce transfer learning in the construction of the intelligent models, where knowledge is learned from the related scenes (source domains) to enhance the performance of model trained in the current scene (target domain). Although transfer learning has been used in EEG signal identification, many existing transfer learning techniques are designed only for a specific intelligent model, which limit their applicability to other classical intelligent models. To extend the scope of application, the generalized hidden-mapping transductive learning method is proposed to realize transfer learning for several classical intelligent models, including feedforward neural networks, fuzzy systems, and kernelized linear models. These intelligent models can be trained effectively by the proposed method even though the data available are insufficient for model training, and the generalization abilities of the trained model is also enhanced by transductive learning. A number of experiments are carried out to demonstrate the effectiveness of the proposed method in epileptic EEG recognition. The results show that the method is highly competitive or superior to some existing state-of-the-art methods.

Transfer learning can address the learning tasks of unlabeled data in the target domain by leveraging plenty of labeled data from a different but related source domain. A core issue in transfer learning is to learn a shared feature space where the distributions of the data from the two domains are matched. This learning process can be named as transfer representation learning (TRL). Feature transformation methods are crucial to ensure the success of TRL. The most commonly used feature transformation method in TRL is kernel-based nonlinear mapping to the high-dimensional space, followed by linear dimensionality reduction. But the kernel functions are lack of interpretability, and it is difficult to select kernel functions. To this end, this article proposes a more intuitive and interpretable method, called TRL with TSK-FS (TRL-TSK-FS), by combining TSK fuzzy system (TSK-FS) with transfer learning. Specifically, TRL-TSK-FS realizes TRL from two aspects. On one hand, the data in the source and target domains are transformed into the fuzzy feature space where the distribution distance of the data between the two domains is minimized. On the other hand, discriminant information and geometric properties of the data are preserved by linear discriminant analysis and principal component analysis. A further advantage is that nonlinear transformation is realized in the proposed method by constructing fuzzy mapping with the antecedent part of the TSK-FS instead of kernel functions, which are difficult to be selected. Extensive experiments are conducted on text and image datasets to demonstrate the superiority of the proposed method.