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

Saha, Anuradha; Konar, Amit; Nagar, Atulya K.

doi:10.1109/tetci.2017.2750761

Cited by 51 publications

(18 citation statements)

References 55 publications

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“…The rest of training and testing are similar to the present work. Table-V includes the results of ' E obtained by the 2 proposed T2FS based mapping techniques against traditional type-1 and type-2 fuzzy [51][52][53], [65][66] algorithms, standard deep learning algorithms, including Long Short-Term Memory (LSTM) [67] and Convolutional Neural Network (CNN) [68], and traditional non-fuzzy mapping algorithms including N-th order Polynomial regression [75] E of ~ 1.5%. In Table-V, we also observe that the IT2FS based mapping technique takes the smallest run-time (~34 ms), when compared with the other mapping methods.…”

Section: A Performance Analysis Of the Proposed T2fs Methodsmentioning

confidence: 99%

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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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Section: A Performance Analysis Of the Proposed T2fs Methodsmentioning

confidence: 99%

“…Thus type-2 fuzzy logic is expected to serve well in functional mapping at higher level neural learning. Two distinct varieties of type-2 fuzzy sets are widely being used in the literature [50][51][52][53], [65][66]. They are wellknown as Interval Type-2 Fuzzy Sets (IT2FS) [50] and General Type-2 Fuzzy Sets (GT2FS) [51].…”

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.

Self Cite

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“…The EEG recordings contain cortical potentials, which occur during various mental processes [2]. These signals comprise of different frequency sub-bands: Delta (4 Hz), Theta (4-7 Hz), Alpha or mu (8-12 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (30-100 Hz) bands, to facilitate ease of analysis. Studies presented in [3,4] found out that mu and beta rhythms are more sensitive to correct and incorrect hand grips and respond strongly especially over motor and pre-motor cortex areas of brain.…”

Section: Introductionmentioning

confidence: 99%

Effect of hand grip actions on object recognition process: a machine learning-based approach for improved motor rehabilitation

Mishra

Sharma

Kumar

et al. 2020

Neural Comput & Applic

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Brain computer interface (BCI) is the current trend in technology expansion as it provides an easy interface between human brain and machine. The demand for BCI based applications is growing tremendously and efforts are in progress to deploy BCI devices for real world applications. One of the widely known applications of BCI technology is rehabilitation in which BCI devices can provide various types of assistance to specially-abled persons. In this paper the effect of hand actions on objects is analyzed for motor related mental task. The proposed approach analysis electroencephalogram (EEG) based brain activity which was captured for images shown with different gripping actions on objects. The EEG recordings are first pre-processed, followed by extraction of epochs and frequency bands using discrete wavelet transform (DWT), afterwards feature extraction followed by training and classification steps are performed for classifying the grip action into congruent (correct) and incongruent (incorrect) grip categories. The proposed work makes use of average power and relative wavelet energy as discriminating features which are then fed to train an artificial neural network for automatically classifying the incoming EEG patterns into correct or incorrect object hand grips. The performance evaluation of proposed system is done on real EEG data set obtained from 14 subjects. Experimental results have shown an accuracy of 75%. Also, to evaluate the effectiveness of our work, a comparison of our work with other state of art works reported by different authors is presented at the end. The results show the effectiveness of proposed approach and suggest further that the system can be used for analyse and train subjects having motorrelated disabilities for perceiving correct or incorrect hand grips on objects.

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“…Techniques based on Computational Intelligence (CI), e.g., artificial neural networks, fuzzy systems, and evolutionary computation, have achieved remarkable results in modelling, learning, searching, and optimisation problems for smart city applications [18], [19], [20]. Deep learning, a relatively "young" learning paradigm in the CI family, has in fact its origin from Artificial Neural Networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen

Wang

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

121

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Rapid urbanisation has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted towards processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and healthcare. Techniques from computational intelligence have been applied to process and analyse such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.

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

Abstract: Abstract-

Cited by 51 publications

References 55 publications

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

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

Effect of hand grip actions on object recognition process: a machine learning-based approach for improved motor rehabilitation

A Survey on an Emerging Area: Deep Learning for Smart City Data

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