Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets

Khasnobish, Anwesha; Konar, Amit; Tibarewala, D. N.; Nagar, Atulya K.

doi:10.1109/tnsre.2016.2580580

Cited by 26 publications

(14 citation statements)

References 93 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: 58%

“…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%

“…In [20], [21] the authors proposed two interesting works on incremental type-2 meta-cognitive learning machines that autonomously detect what, how and when to learn. In recent times, an increasing interest to classify brain signals is noticed in research community [26], [27]. For example, Wang et al selected random forest algorithm for epilepsy detection for its superior performance over its three competitors, including decision tree and support vector machine (SVM) based realizations of both decision tree and random forest [28].…”

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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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Section: C3 Pre-processing and Filteringsupporting

confidence: 58%

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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“…Machine learning models have successfully been used in Brain-Computer Interfaces (BCI) to handle two fundamental problems: i) classification of brain signals for different cognitive activities/malfunctioning [52], [82][83][84] and ii) synthesis of the functional mapping of the active brain lobes from their measured input-output [77]. This paper aims at serving the second problem.…”

Section: Introductionmentioning

confidence: 99%

Mimicking Short-Term Memory in Shape-Reconstruction Task Using an EEG-Induced Type-2 Fuzzy Deep Brain Learning Network

Ghosh

Konar

Rakshit

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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The paper attempts to model short-term memory (STM) for shape-reconstruction tasks by employing a 4-stage deep brain leaning network (DBLN), where the first 2 stages are built with Hebbian learning and the last 2 stages with Type-2 Fuzzy logic. The model is trained stage-wise independently with visual stimulus of the object-geometry as the input of the first stage, EEG acquired from different cortical regions as input and output of respective intermediate stages, and recalled object-geometry as the output of the last stage. Two error feedback loops are employed to train the proposed DBLN. The inner loop adapts the weights of the STM based on a measure of error in model-predicted response with respect to the object-shape recalled by the subject. The outer loop adapts the weights of the iconic (visual) memory based on a measure of error of the model predicted response with respect to the desired object-shape. In the test phase, the DBLN model reproduces the recalled object shape from the given input object geometry. The motivation of the paper is to test the consistency in STM encoding (in terms of similarity in network weights) for repeated visual stimulation with the same geometric object. Experiments undertaken on healthy subjects, yield high similarity in network weights, whereas patients with pre-frontal lobe Amnesia yield significant discrepancy in the trained weights for any two trials with the same training object. This justifies the importance of the proposed DBLN model in automated diagnosis of patients with learning difficulty. The novelty of the paper lies in the overall design of the DBLN model with special emphasis to the last 2 stages of the network, built with vertical slice based type-2 fuzzy logic, to handle uncertainty in function approximation (with noisy EEG data). The proposed technique outperforms the state-of-the-art functional mapping algorithms with respect to the (pre-defined outer loop) error metric, computational complexity and runtime.

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“…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. Herein we are interested in hidden information of brain state-drift occurring in both simulated and real-world human-system interaction.…”

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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Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets

Abstract: Abstract-In

Cited by 26 publications

References 93 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

Mimicking Short-Term Memory in Shape-Reconstruction Task Using an EEG-Induced Type-2 Fuzzy Deep Brain Learning Network

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

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