Recognition of Epileptic EEG Signals Using a Novel Multiview TSK Fuzzy System

Jiang, Yizhang; Deng, Zhaohong; Chung, Fu-Lai; Wang, Guanjin; Qian, Pengjiang; Choi, Kup‐Sze; Wang, Shitong

doi:10.1109/tfuzz.2016.2637405

Cited by 165 publications

(72 citation statements)

References 48 publications

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“…10 and 11. It is confirmed from both the figures that alpha band (8-13 Hz) is associated during visual alertness and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) band is active during motor execution tasks.…”

Section: C3 Pre-processing and Filteringsupporting

confidence: 57%

“…The acquired EEGs from pre-frontal/frontal, parietal and motor cortex regions are pre-processed using band pass filters (BPFs) of suitable frequency bands. VA being more prominent in alpha band (~8-13 Hz) [41] and MP/ME being relatively more active in mu-(8-13 Hz) [42] and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [43] bands, we used BPFs of required pass bands. More review on EEG channel selection and frequency band selection are provided in [44], [45].…”

Section: System Design and Integrationmentioning

confidence: 99%

“…The new learning paradigms introduced include extreme learning machines [19], active/incremental learning [20], [21], transfer learning [22], EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers Anuradha Saha, Member, IEEE, Amit Konar, Senior Member, IEEE and Atulya K. Nagar [23] and multi-view learning [24] techniques. For example, Deng et al employed extreme learning algorithm to adapt parameters in the consequent of type-2 fuzzy rules to improve generalization performance of the resulting system [19].…”

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confidence: 99%

See 2 more Smart Citations

EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Saha

Konar

Nagar

2017

IEEE Trans. Emerg. Top. Comput. Intell.

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Section: C3 Pre-processing and Filteringsupporting

confidence: 57%

Section: System Design and Integrationmentioning

confidence: 99%

mentioning

confidence: 99%

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EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Saha

Konar

Nagar

2017

IEEE Trans. Emerg. Top. Comput. Intell.

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“…There are typically two forms of knowledge representation in fuzzy rule-based inference systems: Mandani type models [4] and Takagi-Sugeno (TSK) type models [5]. Mandani models are classical fuzzy systems which have been popular in many real world applications [6], while TSK ones have also played an increasingly important role in such applications, including for example, stock market prediction [7] and EGG signals recognition [8]. The success of TSK type fuzzy inference systems is largely owing to their capability of approximating complex nonlinear functions [9].…”

Section: Introductionmentioning

confidence: 99%

ANFIS Construction With Sparse Data via Group Rule Interpolation

Yang

Shang

et al. 2021

IEEE Trans. Cybern.

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A major assumption for constructing an effective ANFIS (Adaptive-Network-based Fuzzy Inference System) is that sufficient training data is available. However, in many real world applications, this assumption may not hold, thereby requiring alternative approaches. In light of this observation, this research focusses on automated construction of ANFISs in an effort to enhance the potential of Takagi-Sugeno fuzzy regression models, for situations where only limited training data is available. In particular, the proposed approach works by interpolating a group of fuzzy rules in a certain given domain with the assistance of existing ANFISs in its neighbouring domains. The construction process involves a number of computational mechanisms, including: a rule dictionary which is created by extracting rules from the existing ANFISs; a group of rules which are interpolated by exploiting the local linear embedding algorithm to build an intermediate ANFIS; and a fine-tuning method which refines the resulting intermediate ANFIS. Experimental evaluation on both synthetic and real world datasets is reported, demonstrating that when facing the data shortage situations, the proposed approach helps significantly improve the performance of the original ANFIS modelling mechanism.

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“…the authors of [2] used SONFIN to achieve subject-dependent drowsiness prediction. TSK-type fuzzy system was introduced to recognize epileptic EEG signals in paper [3]. The interval type-2 fuzzy used in study [4] is also a good technique to handle uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

Chang

Wang

Wu³

et al. 2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy.

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Recognition of Epileptic EEG Signals Using a Novel Multiview TSK Fuzzy System

Cited by 165 publications

References 48 publications

EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

ANFIS Construction With Sparse Data via Group Rule Interpolation

Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

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