769 research outputs found
An efficient emotion classification system using EEG
Emotion classification via Electroencephalography (EEG) is used to find the relationships between EEG signals and human emotions. There are many available channels, which consist of electrodes capturing brainwave activity. Some applications may require a reduced number of channels and frequency bands to shorten the computation time, facilitate human comprehensibility, and develop a practical wearable. In prior research, different sets of channels and frequency bands have been used. In this study, a systematic way of selecting the set of channels and frequency bands has been investigated, and results shown that by using the reduced number of channels and frequency bands, it can achieve similar accuracies. The study also proposed a method used to select the appropriate features using the Relief F method. The experimental results of this study showed that the method could reduce and select appropriate features confidently and efficiently. Moreover, the Fuzzy Support Vector Machine (FSVM) is used to improve emotion classification accuracy, as it was found from this research that it performed better than the Support Vector Machine (SVM) in handling the outliers, which are typically presented in the EEG signals. Furthermore, the FSVM is treated as a black-box model, but some applications may need to provide comprehensible human rules. Therefore, the rules are extracted using the Classification and Regression Trees (CART) approach to provide human comprehensibility to the system. The FSVM and rule extraction experiments showed that The FSVM performed better than the SVM in classifying the emotion of interest used in the experiments, and rule extraction from the FSVM utilizing the CART (FSVM-CART) had a good trade-off between classification accuracy and human comprehensibility
An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing
Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure
Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK
High-order Takagi-Sugeno-Kang (TSK) fuzzy classifiers possess powerful
classification performance yet have fewer fuzzy rules, but always be impaired
by its exponential growth training time and poorer interpretability owing to
High-order polynomial used in consequent part of fuzzy rule, while Low-order
TSK fuzzy classifiers run quickly with high interpretability, however they
usually require more fuzzy rules and perform relatively not very well. Address
this issue, a novel TSK fuzzy classifier embeded with knowledge distillation in
deep learning called HTSK-LLM-DKD is proposed in this study. HTSK-LLM-DKD
achieves the following distinctive characteristics: 1) It takes High-order TSK
classifier as teacher model and Low-order TSK fuzzy classifier as student
model, and leverages the proposed LLM-DKD (Least Learning Machine based
Decoupling Knowledge Distillation) to distill the fuzzy dark knowledge from
High-order TSK fuzzy classifier to Low-order TSK fuzzy classifier, which
resulting in Low-order TSK fuzzy classifier endowed with enhanced performance
surpassing or at least comparable to High-order TSK classifier, as well as high
interpretability; specifically 2) The Negative Euclidean distance between the
output of teacher model and each class is employed to obtain the teacher
logits, and then it compute teacher/student soft labels by the softmax function
with distillating temperature parameter; 3) By reformulating the
Kullback-Leibler divergence, it decouples fuzzy dark knowledge into target
class knowledge and non-target class knowledge, and transfers them to student
model. The advantages of HTSK-LLM-DKD are verified on the benchmarking UCI
datasets and a real dataset Cleveland heart disease, in terms of classification
performance and model interpretability
Interpretable and Robust AI in EEG Systems: A Survey
The close coupling of artificial intelligence (AI) and electroencephalography
(EEG) has substantially advanced human-computer interaction (HCI) technologies
in the AI era. Different from traditional EEG systems, the interpretability and
robustness of AI-based EEG systems are becoming particularly crucial. The
interpretability clarifies the inner working mechanisms of AI models and thus
can gain the trust of users. The robustness reflects the AI's reliability
against attacks and perturbations, which is essential for sensitive and fragile
EEG signals. Thus the interpretability and robustness of AI in EEG systems have
attracted increasing attention, and their research has achieved great progress
recently. However, there is still no survey covering recent advances in this
field. In this paper, we present the first comprehensive survey and summarize
the interpretable and robust AI techniques for EEG systems. Specifically, we
first propose a taxonomy of interpretability by characterizing it into three
types: backpropagation, perturbation, and inherently interpretable methods.
Then we classify the robustness mechanisms into four classes: noise and
artifacts, human variability, data acquisition instability, and adversarial
attacks. Finally, we identify several critical and unresolved challenges for
interpretable and robust AI in EEG systems and further discuss their future
directions
Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement
Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input-output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. © 2015 Elsevier Ltd
Fuzzy rule-based alertness state classification based on the optimization of EEG rhythm/channel combinations
This paper presents a method for automatically selecting the optimal EEG rhythm/channel combination capable of classifying the different human alertness states. We considered four alertness states, namely 'engaged', 'calm', 'drowsy', and 'asleep'. Energies associated with the conventional EEG rhythms, δ, θ, α, ß and γ, extracted from overlapping segments of the different EEG channels were used as features. The proposed method is a two-stage process. In the first stage, the optimal brain regions, represented by a set of EEG channels, are identified. In the second stage, a fuzzy rule-based alertness classification system (FRBACS) is developed to select the optimal EEG rhythms extracted from the previously selected EEG channels. The IF-THEN rules used in FRBACS are constructed using a novel bi-level differential evolution (DE) based search algorithm. Unlike most of the existing classification methods, the proposed classification approach reveals easy to interpret rules that describe each of the alertness states
A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance
Model transparency, label correlation learning and the robust-ness to label
noise are crucial for multilabel learning. However, few existing methods study
these three characteristics simultaneously. To address this challenge, we
propose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) with
three mechanisms. First, we design a soft label learning mechanism to reduce
the effect of label noise by explicitly measuring the interactions between
labels, which is also the basis of the other two mechanisms. Second, the
rule-based TSK FS is used as the base model to efficiently model the inference
relationship be-tween features and soft labels in a more transparent way than
many existing multilabel models. Third, to further improve the performance of
multilabel learning, we build a correlation enhancement learning mechanism
based on the soft label space and the fuzzy feature space. Extensive
experiments are conducted to demonstrate the superiority of the proposed
method.Comment: This paper has been accepted by IEEE Transactions on Fuzzy System
A Review on the Development of Fuzzy Classifiers with Improved Interpretability and Accuracy Parameters
This review paper of fuzzy classifiers with improved interpretability and accuracy param-eter discussed the most fundamental aspect of very effective and powerful tools in form of probabilistic reasoning, The fuzzy logic concept allows the effective realization of ap-proximate, vague, uncertain, dynamic, and more realistic conditions, which is closer to the actual physical world and human thinking. The fuzzy theory has the competency to catch the lack of preciseness of linguistic terms in a speech of natural language. The fuzzy theory provides a more significant competency to model humans like com-mon-sense reasoning and conclusion making to fuzzy set and rules as good membership function. Also, in this paper reviews discussed the evaluation of the fuzzy set, type-1, type-2, and interval type-2 fuzzy system from traditional Boolean crisp set logic along with interpretability and accuracy issues in the fuzzy system
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