The diagnosis and treatment of epilepsy necessitate the precise identification and classification of electroencephalogram (EEG) signals. However, EEG samples from different medical institutions often exhibit variability due to factors such as institutional characteristics, geographic locations, and the professional levels of physicians. This variability limits the widespread application of existing methods in small or single medical institutions, as they typically rely on large-scale and high-quality datasets. To rapidly assist small or single medical institutions in constructing models for the diagnosis and classification of epileptic EEG signals that are both highly generalizable and interpretable while ensuring patient privacy, this article proposes an innovative learning framework named Multicenter Knowledge Transfer Calibration with Rapid 0th-Order TSK Fuzzy System (MKTC-R0T). This method employs the 0th-order TSK fuzzy system as the baseline model for each center and integrates a knowledge transfer calibration strategy within a multicenter learning framework, aiming to enhance the model's generalizability and classification accuracy in the face of inconsistent sample quality and sample heterogeneity. Specifically, MKTC-R0T first establishes a base center model in a large medical institution, and then, by imitating the forgetting mechanism of the human brain, a portion of the knowledge at the base center is randomly forgotten, while the remaining knowledge is utilized to assist the auxiliary centers in rapidly deploying models. Ultimately, through a knowledge integration strategy, all centers collectively guide the target center in building an efficient linear system for the diagnosis and classification of epileptic EEG signals. Extensive experiments conducted on 12 epilepsy EEG signal datasets have validated that MKTC-R0T outperforms other typical algorithms in terms of running time, deployment speed, rule complexity, and the model's generalization and robustness, which demonstrates the substantial potential of MKTC-R0T in the field of epilepsy EEG signal diagnosis and classification.

Affective brain computer interface (ABCI) enables machines to perceive, understand, express and respond to people’s emotions. Therefore, it is expected to play an important role in emotional care and mental disorder detection. EEG signals are most frequently adopted as the physiology measurement in ABCI applications. Eye blinking and movements introduce lots of artifacts into raw EEG data, which seriously affect the quality of EEG signal and the subsequent emotional EEG feature engineering and recognition. In this paper, we propose a fully automatic and unsupervised ocular artifact identification and removal algorithm named automated canonical correlation analysis (CCA)-multi-channel wiener filter (MWF) (ACCAMWF). Firstly, spatial distribution entropy (SDE) and spectral entropy (SE) are computed to automatically annotate artifact segments. Then, CCA algorithm is used to extract neural signal from artifact contaminated data to further supplement the clean EEG data. Finally, MWF is trained to remove ocular artifacts from multiple channel EEG data adaptively. Extensive experiments have been carried out on semi-simulated EEG/EOG dataset and real eye blinking-contaminated EEG dataset to verify the effectiveness of our method when compared to two state-of-the-art algorithms. The results clearly demonstrate that ACCAMWF is a promising solution for removing EOG artifacts from emotional EEG data.

This paper proposes a CNN-based born-again Takagi-Sugeno-Kang (TSK) fuzzy classifier denoted as CNNBaTSK. CNNBaTSK achieves the following distinctive characteristics: 1) CNNBaTSK provides a new perspective of knowledge distillation with a non-iterative learning method (least learning machine with knowledge distillation, LLM-KD) to solve the consequent parameters of fuzzy rule, where consequent parameters are trained jointly on the ground-truth label loss, knowledge distillation loss, and regularization term; 2) with the inherent advantage of the fuzzy rule, CNNBaTSK has the capability to express the dark knowledge acquired from the CNN in an interpretable manner. Specifically, the dark knowledge (soft label information) is partitioned into five fixed antecedent fuzzy spaces. The centers of each soft label information in different fuzzy rules are {0, 0.25, 0.5, 0.75, 1}, which may have corresponding linguistic explanations: { very low, low, medium, high, very high }. For the consequent part of the fuzzy rule, the original features are employed to train the consequent parameters that ensure the direct interpretability in the original feature space. The experimental results on the benchmark datasets and the CHB-MIT EEG dataset demonstrate that CNNBaTSK can simultaneously improve the classification performance and model interpretability.

For many application scenarios where raw and even multidomain training data can be easily collected, and at the same time, validation data (as ground-truth data) are available, it becomes naturally desirable for us to perform an enhanced classification/prediction on only validation data with the appropriate leverage of training data. In this article, a novel ensemble framework EP-TSK-FK of Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers, is proposed to achieve the following distinctive characteristics: 1) each interpretable TSK fuzzy subclassifier on each training subset can be quickly built in parallel such that its outputs provide the values of the corresponding augmented features of the original validation data space; 2) as a novel ensemble method of fuzzy subclassifiers, EP-TSK-FK trains all the interpretable TSK fuzzy subclassifiers only once and does not explicitly reuse them while predicting a testing sample, which thereby reduces the computational complexity of the ensemble process for prediction; 3) after running the proposed iterative fuzzy c-means clustering algorithm iterative fuzzy C-means clustering (IFCM) on the augmented validation data to obtain the representative centroids, the fast classification/prediction of EP-TSK-FK on the testing samples is realized by using the k-nearest neighbor (KNN) method on the representative centroids with the original features; and 4) enhanced classification performance by the IFCM & KNN method is theoretically revealed, and the experimental results on the benchmarking datasets indicate the effectiveness of EP-TSK-FK and its parallel learning method in the sense of enhanced classification performance, running time, and interpretability.